Field intercomparison of the gas/particle partitioning of … · 2018. 11. 30. · CONTACT Jose L. Jimenez [email protected] Department of Chemistry and Biochemistry, and

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Aerosol Science and Technology

ISSN: 0278-6826 (Print) 1521-7388 (Online) Journal homepage: http://www.tandfonline.com/loi/uast20

Field intercomparison of the gas/particlepartitioning of oxygenated organics during theSouthern Oxidant and Aerosol Study (SOAS) in2013

Samantha L. Thompson, Reddy L. N. Yatavelli, Harald Stark, Joel R. Kimmel,Jordan E. Krechmer, Douglas A. Day, Weiwei Hu, Gabriel Isaacman-VanWertz,Lindsay Yee, Allen H. Goldstein, M. Anwar H. Khan, Rupert Holzinger, NathanKreisberg, Felipe D. Lopez-Hilfiker, Claudia Mohr, Joel A. Thornton, John T.Jayne, Manjula Canagaratna, Douglas R. Worsnop & Jose L. Jimenez

To cite this article: Samantha L. Thompson, Reddy L. N. Yatavelli, Harald Stark, Joel R. Kimmel,Jordan E. Krechmer, Douglas A. Day, Weiwei Hu, Gabriel Isaacman-VanWertz, Lindsay Yee, AllenH. Goldstein, M. Anwar H. Khan, Rupert Holzinger, Nathan Kreisberg, Felipe D. Lopez-Hilfiker,Claudia Mohr, Joel A. Thornton, John T. Jayne, Manjula Canagaratna, Douglas R. Worsnop &Jose L. Jimenez (2016): Field intercomparison of the gas/particle partitioning of oxygenatedorganics during the Southern Oxidant and Aerosol Study (SOAS) in 2013, Aerosol Science andTechnology, DOI: 10.1080/02786826.2016.1254719

To link to this article: http://dx.doi.org/10.1080/02786826.2016.1254719

View supplementary material Accepted author version posted online: 01Nov 2016.Published online: 01 Nov 2016.

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Field intercomparison of the gas/particle partitioning of oxygenated organicsduring the Southern Oxidant and Aerosol Study (SOAS) in 2013

Samantha L. Thompsona, Reddy L. N. Yatavellia,*, Harald Starka,b, Joel R. Kimmelb,c, Jordan E. Krechmer a,Douglas A. Daya, Weiwei Hua, Gabriel Isaacman-VanWertzd,**, Lindsay Yeed, Allen H. Goldsteind,e,M. Anwar H. Khanf, Rupert Holzingerf, Nathan Kreisbergg, Felipe D. Lopez-Hilfikerh, Claudia Mohrh,***,Joel A. Thorntonh, John T. Jayneb, Manjula Canagaratnab, Douglas R. Worsnop b, and Jose L. Jimenez a

aDepartment of Chemistry and Biochemistry, and Cooperative Institute for Research in the Environmental Sciences (CIRES), University ofColorado, Boulder, Colorado, USA; bAerodyne Research Inc., Billerica, Massachusetts, USA; cTofwerk AG, Thun, Switzerland; dDepartment ofEnvironmental Sciences, Policy and Management, University of California, Berkeley, California, USA; eDepartment of Civil and EnvironmentalEngineering, University of California, Berkeley, California, USA; fInstitute for Marine and Atmospheric Research Utrecht, Utrecht, The Netherlands;gAerosol Dynamics, Inc., Berkeley, California, USA; hDepartment of Atmospheric Sciences, University of Washington, Seattle, Washington, USA

ARTICLE HISTORYReceived 30 December 2015Accepted 1 October 2016

ABSTRACTWe present results of the first intercomparison of real-time instruments for gas/particle partitioningof organic species. Four recently-developed instruments that directly measure gas/particlepartitioning in near-real time were deployed in Centreville, Alabama during the Southern OxidantAerosol Study (SOAS) in 2013. Two instruments were filter inlet for gases and aerosols high-resolution chemical ionization mass spectrometers (FIGAERO-HRToF-CIMS) with acetate (A-CIMS)and iodide (I-CIMS) ionization sources, respectively; the third was a semi-volatile thermal desorptionaerosol GC-MS (SV-TAG); and the fourth was a high-resolution thermal desorption proton-transferreaction mass spectrometer (HR-TD-PTRMS). Signals from these instruments corresponding toseveral organic acids were chosen for comparison. The campaign average partitioning fractionsshow good correlation. A similar level of agreement with partitioning theory is observed. Thus theintercomparison exercise shows promise for these new measurements, as well as some confidenceon the measurement of low versus high particle-phase fractions. However, detailed comparisonshow several systematic differences that lie beyond estimated measurement errors. Thesedifferences may be due to at least eight different effects: (1) underestimation of uncertainties underlow signal-to-noise; (2) inlet and/or instrument adsorption/desorption of gases; (3) differences inparticle size ranges sampled; (4) differences in the methods used to quantify instrumentbackgrounds; (5) errors in high-resolution fitting of overlapping ion groups; (6) differences in thespecies included in each measurement due to different instrument sensitivities; and differences in(7) negative or (8) positive thermal decomposition (or ion fragmentation) artifacts. The availabledata are insufficient to conclusively identify the reasons, but evidence from these instruments andavailable data from an ion mobility spectrometer shows the particular importance of effects 6–8 inseveral cases. This comparison highlights the difficulty of this measurement and its interpretation ina complex ambient environment, and the need for further improvements in measurementmethodologies, including isomer separation, and detailed study of the possible factors leading tothe observed differences. Further intercomparisons under controlled laboratory and field conditionsare strongly recommended.

EDITORPaul Ziemann

1. Introduction

Atmospheric aerosols have important effects on humanhealth (Pope et al. 2009), visibility (Watson 2002), andthe Earth’s climate (Stocker et al. 2013). Aerosols affectclimate directly by scattering or absorbing light and

indirectly by altering cloud brightness, lifetime, and pre-cipitation (Stocker et al. 2013). Recently, a long-termimpact of aerosols on biogeochemical cycles has alsobeen proposed with a radiative forcing comparable tothat of the aerosol direct effect (Mahowald 2011).

CONTACT Jose L. Jimenez [email protected] Department of Chemistry and Biochemistry, and Cooperative Institute for Research in theEnvironmental Sciences (CIRES), University of Colorado, UCB 216, CIRES Bldg., Room 318, Boulder, CO 80302, USA.*Current affiliation: California Air Resources Board, El Monte, California, USA**Current affiliation: Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA***Current affiliation: Karlsruhe Institute of Technology (KIT), Karlsruhe, GermanyColor versions of one or more of the figures in the article can be found online at www.tandfonline.com/uast.

Supplemental data for this article can be accessed on the publisher’s website.© 2016 American Association for Aerosol Research

AEROSOL SCIENCE AND TECHNOLOGYhttp://dx.doi.org/10.1080/02786826.2016.1254719

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Submicron particles are the more active climatically andmost important for human health impact, and organicaerosols (OA) represent a substantial fraction of theirmass (Kanakidou et al. 2005; Zhang et al. 2007; Jimenezet al. 2009).

OA can be classified into primary OA (POA) that isdirectly emitted by both natural and anthropogenic sour-ces and secondary OA (SOA) that is formed by oxidationof gas-phase compounds followed by the gas-to-particlepartitioning of less volatile products (Pankow 1994;Donahue et al. 2006; Jimenez et al. 2009) or by aqueous-phase chemistry (Lim et al. 2010; Ervens 2015). Studieshave demonstrated that a major fraction of OA is SOAacross urban, rural and remote sites (de Gouw 2005;Hallquist et al. 2009; Jimenez et al. 2009). The formation,aging, chemical properties, and lifetime of OA are notwell understood (Goldstein and Galbally 2007; de Gouwand Jimenez 2009; Hallquist et al. 2009), and these largeuncertainties often lead to discrepancies between modelsand measurements of aerosol loading (de Gouw 2005;Volkamer et al. 2006; Dzepina et al. 2009; Tsigaridiset al. 2014).

SOA has been long assumed to be semivolatile (Odumet al. 1996). In the last few years, however, several studieshave suggested that some of the model-measurementdiscrepancies might be due to nonequilibrium gas/parti-cle partitioning caused by kinetic limitations due to thepresence of glassy or semi-solid phases (Vaden et al.2010, 2011; Virtanen et al. 2010). Whether and whensuch limitations apply is a focus of present research (Vir-tanen et al. 2010; Vaden et al. 2010, 2011; Shiraiwa et al.2011; Perraud et al. 2012; Price et al. 2013; Renbaum-Wolff et al. 2013; Saleh et al. 2013; O’Brien et al. 2014;Yatavelli et al. 2014a,b; Upshur et al. 2014; Lopez-Hil-fiker et al. 2016). To better understand and predict for-mation, growth, evolution, and losses of SOA, it iscritical to understand gas/particle partitioning of organicspecies in the real atmosphere. Most studies to dateaddressing the kinetic limitations to partitioning havebeen indirect methods and without chemical specificity.Accurate, direct, and fast time-resolution measurementsof the gas/particle partitioning of key OA species areneeded to resolve these discrepancies and improve therepresentation of OA in atmospheric models.

Recently the direct in situ measurement of the gas/particle partitioning of organic species and/or groups ofspecies has become possible due to new instrumentaldevelopments (Holzinger et al. 2010; Yatavelli andThornton 2010; Yatavelli et al. 2012; Lopez-Hilfiker et al.2013; Rollins et al. 2013; Isaacman et al. 2014). However,different measurements of gas and particle-phase at thesame time by the same instrument (and therefore Fp)have not been previously compared.

Semivolatile organic acids are abundant in the atmo-sphere (Chebbi and Carlier 1996; Yatavelli et al. 2015)and are important oxidation products of anthropogenicand biogenic volatile organic compounds (VOC) andcontribute to SOA (Veres et al. 2011; Andrews et al.2012; Vogel et al. 2013). Because of their vapor pressurerange, many larger or more oxidized organic acids areexpected to partition between the gas and particle phasesat typical atmospheric OA concentrations (»0.1–100 mgm¡3). However, despite their ubiquity and importance,the gas/particle partitioning dynamics and equilibria oforganic acids are still poorly characterized.

Here, we compare results from four instruments thatdirectly measured gas/particle partitioning of organicacids with high time resolution. Measurements were con-ducted at the ground supersite during the 2013 SOASfield study in the southeastern United States (Hu et al.2015; Carlton et al. 2016). To our knowledge, this is thefirst intercomparison of real-time gas/particle partition-ing to date, whether in a field or laboratory setting.

2. Instrumentation and methods

Four instruments are compared in this study: two filterinlet for gases and aerosols (FIGAERO) high-resolution–time-of-flight–chemical-ionization mass spectrometers(HRToF-CIMS) operated by different research groupsand with different ionization methods, a semi-volatilethermal-desorption aerosol gas chromatograph (SV-TAG), and a high-resolution–thermal-desorption–pro-ton-transfer-reaction mass spectrometer (HR-TD-PTRMS). All four instruments measure partitioning byseparating particle-phase compounds from gas- or gas-plus-particle-phase compounds during sampling andusing thermal desorption to evaporate particle-phasecompounds. Either online analysis (without heating) orthermal desorption are used to analyze gas-phase com-pounds. Details about each instrument can be foundbelow and a diagram of the respective heating profilesexperienced by a typical compound (desorbing at 100�C)is shown in Figure 1. For the CIMS instruments, the tem-perature ramp acts such that the molecules do notencounter a temperature greater than their thermaldesorption temperature, decreasing the likelihood ofthermal decomposition. In the PTRMS (which usesheated transfer lines) and the SV-TAG (which uses atemperature ramp to facilitate flow through the GC col-umn and temperatures up to 250�C for rapid flowthrough valves) higher temperatures are experiencedbefore detection. That might increase the chances ofthermal decomposition, although the residence timesand materials that the species come into contact in thedifferent instruments likely also play a role. As exposure

2 S. L. THOMPSON ET AL.

to higher temperatures in SV-TAG is dominated by thetemperature ramp of the GC, decomposition in thisinstrument is expected to be similar to most other, morecommon chromatographic analyses of the studied com-pounds, such as filter collection and extraction. It hasbeen shown that some isoprene SOA oligomers dodecompose in the FIGAERO-CIMS at temperaturesbelow 100�C, therefore decomposition is not only a hightemperature process (Lopez-Hilfiker et al. 2016). No dry-ing of gas-phase water was carried out for any instru-ment. A summary of each instrument’s separation anddetection method can be found in Table S1.

2.1. Figaero-HRToF-CIMS

Two of the instruments compared in this study use twoversions of the same gas/particle inlet, followed by detec-tion with acetate (A-CIMS) or iodide (I-CIMS) reagentions. These instruments were built by Aerodyne, withcustom modifications by the research groups. The keycomponents of this instrument are described in detailelsewhere (Yatavelli et al. 2012, 2014a; Lopez-Hilfikeret al. 2013). Briefly, it is composed of three stages. Thefirst stage is the filter inlet for gases and aerosols

(FIGAERO), a dual parallel inlet (one for gas and one foraerosol) that was designed to simultaneously sampleatmospheric gases and aerosols on a semi-continuousbasis (Lopez-Hilfiker et al. 2013). The inlet switchesbetween different modes to measure gas and particlephase compounds. In the “sampling” mode, ambient airis drawn through the gas phase inlet and gases are ana-lyzed, while aerosols are simultaneously collected on aPTFE Teflon-membrane filter. During the “desorptionmode,” both atmospheric sampling flows are still drawnthrough their inlets at the same rates (to avoid transientlosses/sources in both inlets due to inlet adsorption/desorption if the flow was interrupted), but are bypasseddirectly into the pumps at the latest possible point beforeentering the instrument. Meanwhile the aerosols arethermally desorbed off the filter and sampled into theinstrument. Aerosol desorption is accomplished by heat-ing ultra-high purity (UHP) nitrogen (N2) at a steadyramp rate of 17�C per min up to 200�C while flowing itover the filter for 10 min (times in this section apply tothe A-CIMS, small differences for the I-CIMS are sum-marized below). The N2 flow is then held at 200�C for 20additional min to ensure that all organic material isremoved from the filter and reduce any carry-over for

Figure 1. Schematic diagram of the temperature profiles that a typical compound would experience in the gas and particle phase ineach instrument. The example compound is assumed to have a volatility such that it would thermally desorb at 100�C. The width of thethermal desorption curve (Faulhaber et al. 2009; Lopez-Hilfiker et al. 2016) is ignored for the purposes of this diagram.

AEROSOL SCIENCE AND TECHNOLOGY 3

the next collection/desorption cycle. Finally, room-tem-perature N2 is passed over the filter for 5 min to cool thefilter and its enclosure before initiating the next aerosolcollection cycle. This entire cycle is repeated continu-ously every 55 min. Every 6th cycle is a “zero cycle,”where UHP nitrogen is sampled through the gas inletand a filter is placed in front of the particle phase inlet,using the same timing as described above for the ambientmeasurements, in order to regularly quantify the instru-ment and inlet backgrounds for both modes of opera-tion. Figure S1 shows an example of the signals observedover a whole cycle for the A-CIMS.

The second stage of the instrument is the chemical ioni-zation region (also known as ion-molecule reaction region,IMR). Chemical ionization (CI) enables sensitive and selec-tive detection of targeted compounds. This region wasoperated with either acetate reagent ion or iodide reagention and is described in the following sections.

The third stage is the high-resolution–time-of-flightmass spectrometer (H-TOF, Tofwerk AG, Thun, Swit-zerland), which rapidly (33 kHz averaged to 1 s) acquiresthe entire mass spectrum. The high resolving power (R> 4000 FWHM at m/z 300 in V ion-path mode as usedhere) of this stage allows for estimation of the elementalcomposition of many of the measured compounds(DeCarlo et al. 2006; Yatavelli et al. 2012). Only the ele-mental composition of the analyte can be determinedusing this method, while structural isomers cannot bedistinguished. In addition the instrument resolution isnot sufficient to resolve all possible ions that could bepresent, which introduces some ambiguity in the analy-sis, especially for small peaks in a isobaric peak ensemble(Stark et al. 2015). The A-CIMS is first described indetail, while the description of the I-CIMS summarizesthe key differences with the A-CIMS.

2.1.1. Acetate CIMSTwo inlet configurations were used for this instrument.In the first configuration during “sampling” mode, ambi-ent air was drawn at 10 standard liters per minute (slpm,273 K and 1 atm) through a 6 m long, 0.95 cm innerdiameter PTFE Teflon tube and then subsampled at theinstrument entrance at 2 slpm, with a total inlet resi-dence time of 2.5 s. Gas-phase species are analyzed con-tinuously during the sampling phase. There is nofiltering of particles in this inlet, but evaporation of parti-cle phase compounds is thought to not contribute sub-stantially to gas phase signal due to low residence time ofparticles in the IMR (Yatavelli and Thornton 2010;Lopez-Hilfiker et al. 2013). Molecules evaporating fromparticles deposited to the IMR surfaces should beapproximately taken into account by the backgroundsubtraction process. Simultaneously, aerosols were

sampled through a PM2.5 cyclone at 10 slpm through a0.95 cm inner diameter copper tube, also approximately6 m in length, with an inlet residence time of 2.5 s, andcollected for 20 min on a PTFE Teflon-membrane filter.Both inlets sampled from approximately 6 meters off theground. A picture of the inlet can be found in Figure S2.A second inlet configuration was implemented on 5 July2013 to allow for much shorter inlet lengths and reducethe potential importance of inlet adsorption and memoryeffects. Results from both inlets are compared below andshow little difference. All data intercompared with otherinstruments from A-CIMS in this article uses the firstconfiguration, due to the timing of when the differentinstruments were operating simultaneously.

Acetate ions [CH3C(O)O¡] abstract a proton from

organic acids, typically without (or with limited) frag-mentation, or form ion-molecule adducts. This CImethod is discussed in detail elsewhere (Veres et al.2008; Bertram et al. 2011; Yatavelli et al. 2012). In thechemical ionization region, 2 slpm of sample gas ismixed at a 90 degree angle with a 2 slpm flow containingthe reagent ions. Acetate reagent ions are formed byflowing 2 slpm UHP N2 containing acetic anhydride(from bubbling the N2 through liquid acetic anhydride)which then pass through a Po-210 ionizer (10 mCi). Aresidence time of »100 ms in the CI region allows forthe reagent ion/sample molecule reactions to proceed(»85 mbar). 0.5 slpm is pulled into the mass spectrome-ter through a critical orifice, while the rest of the flow isexhausted through a pump. Due to the selective chemicalionization scheme used, most molecules detected areassumed to be acids (Veres et al. 2008).

All data were processed using the custom Tofwaresoftware package (version 2.4.3; Tofwerk AG, Thun,Switzerland; Aerodyne Research Inc., Billerica, MA,USA) (Stark et al. 2015) within Igor Pro (version 6.32;Wavemetrics, Inc., Lake Oswego, OR, USA). Exampledata, including a zero cycle to show the backgrounds ofthe instrument, can be found in Figure S1. Figure S3shows how the FIGAERO is calibrated by integrating thetotal thermal desorption signal which led to repeatable,linear calibrations.

2.1.2. I-CIMSThis instrument is conceptually identical to the onedescribed above except for the reagent ion. This instru-ment uses the iodide ion, I¡, as a reagent ion to selec-tively ionize relatively oxidized molecules starting fromCH3I precursor (Huey et al. 1995; Aljawhary et al. 2013;Lee et al. 2014). The instrument was used with a FIG-AERO collector and high-resolution mass spectrometerin the same way as the A-CIMS was described above.The two FIGAERO collectors were built separately


following the same design (by UW for the I-CIMS andby Aerodyne/Colorado for the A-CIMS). The stainlesssteel 25 mm OD particle phase inlet for the I-CIMS was2.7 m long and used a custom inertial impactor with a2 mm cutpoint. The PTFE gas phase inlet (19.05 mmOD) was 1.8 m long. Flow rates were 15.5 slpm for thegas phase and 22 slpm for the particle phase inlet. Withthe I-CIMS, sampling time was kept at 20 min and thedesorption phase at 45 min, with 20 min temperatureramp to 200�C, 20 min soaking at 200�C, and 5 mincooling down to ambient temperature. The sensitivityusing this ion chemistry is dependent on water concen-tration in the chemical ionization region. Since the gasphase is sampled at ambient RH and the particle phase issampled using dry nitrogen, a sensitivity correction wasapplied to account for the effect of water (Lee et al. 2014).

2.2. SV-TAG

A dual-cell semivolatile thermal desorption aerosol gaschromatograph (SV-TAG) with in situ derivatization pro-vided hourly measurement of functionalized semi- andlow-volatility organic compounds in the gas- and particle-phases. A detailed description of this instrument has beenprovided elsewhere (Isaacman et al. 2014), and is onlybriefly summarized here. Sample air is drawn through aninlet and into two identical custommade filter cells for col-lection and thermal desorption (F-CTD cells), which arecollected at the same time, and then analyzed in series.The inlet comprises 2 stages: a “chimney” followed by asub-sample line off the center stream. Air is pulled at»200 slpm through a 38.7 cm wide cleaned steel duct(“chimney”) from a height of »4 m above ground (resi-dence time »10–15 s). The SV-TAG subsamples at 20slpm (10 for each cell) from the center stream of the chim-ney through a cyclone with a size cutoff of 1 mm followedby»1 m of 0.95 cm (3/80) ID clean stainless steel (SS) tub-ing. Each F-CTD cell consists of a high-surface-area pas-sivated SS metal fiber filter (Zhao et al. 2013) in athermally-controlled housing (30�C), which quantitativelycollects gas and particle-phase compounds with a volatilityas high as that of tetradecane (saturation concentration at25�C, C� » 107 mg m¡3) (Zhao et al. 2013). During samplecollection, one cell samples through a 400-channel cylin-drical activated-carbon denuder (30 mm OD £ 40.6 mmlength; MAST Carbon) to remove gas-phase species.Simultaneous collection of total (undenuded) and particle-only (denuded) samples, followed by sequential analysis ofthe samples and subtraction to calculate gas concentra-tions, allows a direct calculation of gas/particle partitioningof the measured species. Calibration occurred through sev-eral injections per day of varying concentrations of liquidstandards, and an internal standard was added to every

sample to control for run-to-run variability in instrumentresponse. Background and zero corrections occurredthrough both regular sampling of N2 to measure gas-phaseartifacts from sample lines, and analysis of F-CTD cellswith no sample collection to confirm instrument zeros.Penetration of gas-phase species through the denuder wastested approximately weekly by placing a PTFE Teflon fil-ter in the sampling line upstream of the denuder, andfound to be negligible in most cases. In the cases where itwas not negligible it was corrected for through the back-ground subtraction.

A typical duty cycle consists of 22 min of sampling onboth cells, injection of standards (2 min), two-steppurge-and-trap desorption (30 to 310�C, 16 min) andchromatographic analysis (14 min: 50 to 330�C at23.6�C/min, 2 min hold) of F-CTD1, and then desorp-tion of F-CTD2 (16 min), and chromatographic analysiswhile collection of the subsequent sample begins afterexactly 60 min from the previous collection start. Duringanalysis, samples are thermally desorbed into a heliumpurge flow saturated with derivatizing agent (MSTFA, N-Methyl-N-(trimethylsilyl)-trifluoroacetamide) in a two-step purge-and-trap method. Conversion of hydroxylgroups into silyl ethers and esters using MSTFA duringthe first stage of desorption allows quantitative analysisof highly polar compounds by gas chromatography/massspectrometry (GC/MS). Analysis was performed using acustom-modified 7890A gas chromatograph (AgilentTechnologies) equipped with a nonpolar column (20 m£ 0.18 mm £ 0.18 um, Rxi-5Sil MS; Restek, Bellefonte,PA, USA) coupled to a 5975C unit-resolution quadrupolemass spectrometer (Agilent Technologies). All chro-matographic data were reduced and analyzed using cus-tom analysis code written in Igor Pro 6.3 (WaveMetrics),which forms the basis of the publicly available softwareTAG ExploreR and iNtegration (Isaacman-VanWertz G,Sueper D (2014) TERN: TAG ExploreR and iNtegration.Available at: https://sites.google.com/site/terninigor).

2.3. HR-TD-PTRMS

This instrument (hereinafter “PTR-MS” for short) con-sists of a modified-commercial PTR-MS instrument(PTR-TOF8000, Ionicon Analytik GmbH, Innsbruck,Austria) with a high mass-resolution time-of-flight massspectrometer (H-TOF, Tofwerk AG, Thun, Switzerland,same TOF analyzer used by A-CIMS and I-CIMS) withseparate gas and aerosol inlets. The aerosol samples werecollected through dual aerosol inlet (copper tubing ofheight» 4m and ID»0.65 cm). The details of the aerosolsampling inlet, collection and analytical methods canbe found in Holzinger et al. (2010). In the aerosol collec-tion channel, particles in the size range 0.07–2 mm were


https://sites.google.com/site/terninigor

collected by impaction on a collection-thermal-desorp-tion (I-CTD) cell. Aerosols collected from a total volumeof 150–220 liters of air were desorbed from the I-CTDcell into a UHP N2 flow of 7 mL min

¡1. Thermal desorp-tion increased the temperature in 7 steps of 50�C incre-ments to a maximum of 350�C with a ramp and dwelltime of 3 min per step followed by an oxidizing step for3 min during which UHP N2 was replaced by syntheticair. The N2 with the desorbed aerosol species was ana-lyzed with the PTR-MS. All transfer lines to the PTR-MSwere made of Sulfinert-coated (Restek) stainless steel andwere heated continuously (200�C) to avoid re-condensa-tion of evaporated organic material. Gas sample wasdrawn at 1 slpm through »20 cm of »0.65 cm ID Silcos-teel tube (Restek) from the center airstream of the chim-ney (steel duct of length »4 m and ID » 38 cm, airflow»200 slpm) which was then passed through a custom-made sampler box containing 3 denuders in series tosample gas-phase species: 1st denuder DB-1 column,0.53 mm £ 5.0 mm for capturing semivolatile organiccompounds (SVOCs), 2nd denuder DB-1 column,0.53 mm £ 5.0 mm for capturing any leftover SVOCs,and 3rd denuder- activated carbon for capturing VOCs.SVOCs and VOCs are sampled through the denuder boxfor 30 min. After sampling, a reverse flow of UHP N2 (70sccm) was applied, and the PTRMS sampled the effluentN2 from the denuder system containing the sampledmolecules. Each denuder was heated by ramping the tem-perature up to 200�C in four steps of 50�C with a rampand hold time of 4 min per step followed by an oxidizingstep for 3 min. The sampler box was then cooled to roomtemperature, to minimize volatilization of the SVOCsduring sampling and protect the collection system fromenvironmental changes. Gas-phase backgrounds weremeasured every 9 h by sampling ambient air through aplatinum catalyst at 500�C, while aerosol backgrounds(sampling through a PTFE Teflon membrane filter,Zefluor 2.0 mm, Pall Corp., New York, NY, USA) weremeasured every other measurement cycle. A total of 12–13 measurement cycles were performed per day. Eachcycle was completed in 107 min and included the analysisof the first I-CTD cell aerosol samples (25 min), second I-CTD cell aerosol samples (background) (25 min), and thedenuder gas samples (57 min). All data were processedusing the PTRwid software following Holzinger (2015).

2.4. Nitrate-ion ion mobility time-of-flight massspectrometer (NO3

¡-IMS-TOF)

In order to gain more information about the presence ofisomers, ion mobility-mass spectrometry (Eiceman andKarpas 2010) measurements were obtained from a drift-tube ion mobility spectrometer coupled to an H-TOF high-

resolution time-of-flight mass spectrometer (IMS-TOF;Tofwerk AG, Thun, Switzerland). The instrument has beendescribed in detail in previous publications (Kaplan et al.2010; Zhang et al. 2014b; Krechmer et al. 2016). The IMS-TOF was deployed during SOAS with a custom-builtnitrate-ion (NO3

¡) chemical ionization source initiatedwith an X-ray ionizer (Hamamatsu Photonics, Hama-matsu, Japan). Data were post-processed and analyzedusing the Tofware IMS data analysis package for Igor Pro.Additional results from the IMS-TOF at the SOAS site aredescribed in (Krechmer et al. 2016). We note that due tothe different ion chemistries and inlet configurations, thespecies detected with the IMS-TOF may not completelyoverlap with those detected by the other instruments.

2.5. Gas/particle partitioning calculations

All gas/particle partitioning measurements are expressedas fractions of a given species (i) in the particle phase(Fp,i), calculated as the ratio of the measured particle-phase concentration to the measured total concentration(gas plus particle) after subtraction of any instrumentbackgrounds, i.e.:

Fp;iD ParticleGasC Particleð Þ ½1�

2.6. Uncertainty estimation

Measurement uncertainties were estimated for eachinstrument based on its particular measurementmethod. Note that the estimated errors do not includeuncharacterized errors that may be factors such asinterfering isomers, thermal decomposition, oligomerinterference, or any uncharacterized sampling arti-facts. For A-CIMS and I-CIMS, the total uncertaintyis calculated as the combination of the estimatedaccuracy and precision:

sFp Dffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiaccuracy2C precision2p ½2�

Particle and gas-phase signals are calculated for theCIMS instruments as

Concentration D Signalraw ¡Backgroundð Þ � Sensitivity:½3�

Each of the three components (signal, background, andsensitivity, for the gas and particle phase measurements)has an accuracy and a precision, resulting in up to 12 com-ponents contributing to the overall Fp uncertainty. Theestimation methods for each of these 12 components canbe found in Table S1 in the online supplementary


information [SI]. All estimated uncertainties (shown aserror bars) are 1s. Further information on the CIMS errorestimates can be found in Lopez-Hilfiker et al. (2013). Rel-ative uncertainties for the CIMS instruments are done ona point by point basis, but on average lie between 15–25%for Fp.

All Fp values from the SV-TAG in the studied caseshave an estimated total relative uncertainty of §15%(1s). Estimation for SV-TAG is discussed in detail in theSupplemental Information of Isaacman et al. (2014). AllPTRMS Fp values have an estimated total relative uncer-tainty of §30% (1s). Further information on the estima-tion of this value can be found in Holzinger et al. (2010b).

2.7. Modeling

Gas/particle partitioning was modeled using equilibriumpartitioning theory for absorptive partitioning into theorganic aerosol phase (Pankow 1994; Donahue et al.2006) with the following equations:

Fp;i D 1C C�i

COA

� �¡ 1½4�

C�i DMi106ziPi760RT

½5�

Where i represents a given species, Fp is fraction in theparticle phase, Ci

� is the saturation mass concentration

(mg m¡3), COA is the organic aerosol mass concentration(mg m¡3), Mi is the molecular weight (g mol¡1), z is theactivity coefficient (assumed D 1), Pi is the pure compo-nent liquid vapor pressure (Torr), R is the universal gasconstant (8.2£10¡5 m3 atm K¡1 mol¡1), and T is theambient temperature (K). Values for C� can be found inTable S2.

The partitioning of compounds to the aqueous phasewas estimated using Henry’s Law:

H D Car

½6�

where H is Henry’s law constant (M atm¡1), Ca is the con-centration of the species in the aqueous phase (Mol L¡1),and r is the partial pressure of the species at equilibrium(atm). Median aerosol liquid water content of 2.9 mg m¡3

(Nguyen et al. 2014) and literature values for Henry’s LawConstants (Compernolle and M€uller 2013) were used.Note that we are using a measured value for liquid watercontent, and modeled values from a separate study are notsignificantly different for the purposes of aqueous phasepartitioning calculations (Guo et al. 2015). The resultsfrom these calculations are discussed below.

2.8. SOAS field study

All the instruments were deployed during the SOAS fieldstudy at the ground supersite in Centreville, Alabama, in

Table 1. Summary of the components in the uncertainty calculation for the two FIGAERO CIMS instruments.

Calculation for A-CIMS Calculation for I-CIMS A-CIMS I-CIMS

Particle Phase ErrorsParticle Signal Accuracy From particle phase calibration.

Repeats of same concentrationSame 9.1% 12%

Particle Background Accuracy Variability of adjacent blankmeasurements

N/A 5%

Particle Sensitivity Accuracy From particle phase calibration.Repeats of same concentrationat different locations anddifferent times

N/A 12%

Particle Signal Precision Standard deviation of steadyparticle phase signal

Same 6.2% 10%

Particle Background Precision Standard deviation of backgroundsignals

Same 20% 2.5%

Particle Sensitivity Precision From repeated calibrations of acompound

Same 24% 20%

Gas Phase ErrorsGas Signal Accuracy From gas phase calibration. Repeats

of same concentrationSame 4.1% 17%

Gas Background Accuracy Variability of adjacent blankmeasurements

N/A 40%

Gas Sensitivity Accuracy St dev of mean calibration values offormic acid at SOAS, and in thelab

N/A 12%

Gas Signal Precision Standard deviation of steady signal Same 22% 1%Gas Background Precision Standard deviation of background

signalsSame 5.5% 12%

Gas Sensitivity Precision From repeated calibration of acompound

Same 20% 10%


the summer of 2013. The study is described elsewhere(Hu et al. 2015; Carlton et al. 2016). We selected a timeperiod for intercomparisons of about nine days, 17–25June 2013, during which all four instruments were sam-pling simultaneously. Figure S4 shows the average diur-nal cycles of temperature (22–28�C), relative humidity(67–96% with sporadic precipitation), and organic aero-sol concentration (4–9 mg m¡3).

3. Results and discussion

The results are separated into two sections. The first sec-tion compares the results from A-CIMS, I-CIMS, and

SV-TAG. The second compares the A-CIMS and thePTRMS. This structure was chosen because the PTRMSdid not report any compounds in common with the SV-TAG or the I-CIMS, and so it can only be compared tothe A-CIMS separately. A table of all the compounds dis-cussed in this article and their elemental formulas is inthe Tables S2 and S3.

3.1. Comparison of A-CIMS, I-CIMS, and SV-TAG

Three acids were positively identified with the SV-TAGfor which an ion with the same molecular formula wasalso observed by the A-CIMS and I-CIMS: pinonic acid

Figure 2. Scatter plots of (a) the average measured Fp (averaging all measurements for each compound), (b) particle-phase concentra-tion, (c) gas-phase concentration, and (d) total concentration over the entire overlap period from SV-TAG and I-CIMS vs. A-CIMS. Eachpoint represents one of the three compounds discussed in Section 3.1. Error bars represent estimated instrumental uncertainties asdescribed in Section 2.5. Regression lines are fixed through the origin and computed using the orthogonal distance regression method(ODR). The A-CIMS was chosen for the x-axis because it is the only instrument that can be compared with all the other instruments (theI-CIMS, SV-TAG, and also the PTRMS). Note that we use rjrj for the I-CIMS/A-CIMS comparison in (d) since r < 0.


(C10H16O3), pinic acid (C9H14O4), and hydroxyl glutaricacid (C5H8O5). These compounds are expected to be thedominant contributor to the observed ions, which is sup-ported by measurements discussed below, but thereremains some uncertainty as to the extent to which thepresence of different isomers between instruments couldconfound these comparisons. The three compoundsspan a large range of vapor pressures as reflected in thelarge range of average Fp values (from all 3 instruments),from 0.04 to 0.88. The CIMS identified these via theirelemental formula, and the TAG by a match of retentiontime and mass spectrum with standards. We note thatthe CIMS measurements may also include other speciesof the same elemental composition, and the same appliesto other species inter-compared and discussed below.There are many possible isomers for this elemental for-mula, several of which are acids (measurable by theA-CIMS) and other oxidized compounds (potentiallymeasurable by the I-CIMS). We first present a summaryof the comparison for the averages of the whole period,followed by a detailed time-resolved comparisons forindividual compounds.

3.1.1. Comparison of whole-period averagesThe average measured Fp for the three common speciesand the entire sampling period are compared in Figure 2a.All three instruments show a consistent trend of increas-ing Fp with decreasing vapor pressure across the entirerange of possible Fp values (0–1), which is consistent withthe trend predicted by partitioning theory (Figure 3).These compounds have been found to agree well with theorganic absorptive partitioning model in a previous studyat a different site using A-CIMS (Yatavelli et al. 2014a).When plotted against the A-CIMS, the average Fp showslopes of 1.86 (r2 D 0.99) and 1.68 (r2 D 0.98) for SV-TAG and I-CIMS, respectively. The I-CIMS reports con-sistent Fp values with the A-CIMS within the estimateduncertainties. The SV-TAG measures higher values thanthe A-CIMS that cannot be explained by the estimateduncertainties, but is within uncertainty of the I-CIMS fortwo of the three compounds. Note that the estimateduncertainties do not include uncharacterized effects as dis-cussed above. All instruments report gas, particle, andtotal concentrations of the same order of magnitude, typi-cally of tens to hundreds of ng m¡3 for the three species.The absolute concentrations measured show varyingdegrees of agreement and correlation. The SV-TAG andA-CIMS show excellent agreement (slope D 1.03 and r2 D0.97) for the average particle phase concentrations(Figure 2b), while the gas-phase measurements showmore scatter, primarily due to a 2.5 times lower pinic acidgas-phase concentration in SV-TAG vs. A-CIMS(Figure 2c). I-CIMS vs. A-CIMS particle phase

concentrations are of the same order but show significantscatter(r2 D 0.27), while the gas-phase concentrations are muchlower in the I-CIMS than in the A-CIMS for the two lowervolatility species. For the total concentrations an anti-cor-relation is observed, which is mainly due to the gas-phasepinic acid measurement. This might be due to the pres-ence of multiple isomers with that elemental formula,which the two instruments measure with different sensi-tivities. Evidence for this effect will be presented in Section3.1.5. Thus the study-average comparison shows mixedresults: Fp values show high correlation, but there are sub-stantial disagreements in some of the concentrations. Inthe next 3 sections, we explore the three comparisons athigh time resolution (one hour), which is the reportinginterval of most of the instruments.

3.1.2. Detailed comparisons for pinonic acidAll three instruments measured C10H16O3 (“pinonicacid,” where the quotes indicate some ambiguity on thetrue species detected, as discussed below). The CIMSinstruments identified it via its elemental formula, andthe SV-TAG by a match of retention time and massspectrum with a standard. We note that the CIMS meas-urements may also include other species of the same ele-mental composition, and the same applies to otherspecies intercompared and discussed below. There aremany possible isomers for this elemental formula, severalof which are acids (measurable by the A-CIMS) andother oxidized compounds (potentially measurable by

Figure 3. Comparison of average Fp values vs. those modeledusing organic absorptive partitioning theory. Regression lines arefixed through the origin (since all instruments performed zerotests and are expected to report zero concentrations when therelevant species are not present) and computed using theorthogonal distance regression method (ODR). See text for moredetails on modeling.


the I-CIMS in addition to pinonic acid and its acid iso-mers). Pinonic acid is an oxidation product of a¡pinene(Szmigielski et al. 2007) and is therefore expected at thisfield site where a-pinene was abundant.

Figure 4 shows the time series, scatter plot, and diur-nal cycle of Fp (top), particle phase concentration (sec-ond row), gas phase concentration (third row) and total(particle C gas) concentration (bottom). This specieswas mostly in the gas phase with an Fp below 7% for thediurnal averages, according to all instruments. Some cor-relation is observed in all comparisons, although with asubstantial amount of scatter, with R2 in the range 0.34-0.80. For the SV-TAG and A-CIMS 40% of the Fp pointsare within a factor of two of each other and for the I-CIMS and A-CIMS 26% of the points are within a factor

of two of each other, illustrating the degree of scatter.The SV-TAG and A-CIMS show better agreement forboth gas and particle concentrations. In contrast, the I-CIMS tends to report lower concentrations than theA-CIMS, albeit with higher correlation than SV-TAG/A-CIMS for the particle concentration. Averaging the indi-vidual data points into diurnal cycles results in smoothvariations in most cases, with comparison trends similarto those already discussed. All the instruments are con-sistent with the organic absorptive partitioning modelprediction that “pinonic acid” is almost entirely in thegas phase, though none are as low as the model, and allshow different diurnal cycles. There is a substantialuncertainty in the model predictions due to large uncer-tainties in vapor pressures (Bilde et al. 2015) and the

Figure 4. “Pinonic Acid.” Time series (left), scatter plots (center) and diurnal cycles (right) of the measured particle-phase fraction (Fp, toprow), and particle phase (2nd row), gas phase (3rd row) and total concentrations (bottom row) for the A-CIMS, I-CIMS, and SV-TAG. Errorbars represent estimated instrumental uncertainties as described in Section 2.5. Fp values modeled with absorptive partitioning are alsoshown in the top row. Regressions lines are fixed through the origin and computed using the orthogonal distance regression method(ODR). The spike in the I-CIMS data is believed to be real, but the reason for it is unclear.


assumption of an activity coefficient of one, which couldaccount for some of the model/measurement differences.Partitioning to the aqueous phase is estimated to be neg-ligible for this species with a modeled aqueous phase Fpof 1.4 £ 10¡8.

3.1.3. Potential reasons for the observed differencesThere are many possible reasons for the disagreementsbetween the instruments beyond the estimated uncertain-ties. All the reasons are enumerated here, and the most rel-evant to latter comparisons will be briefly mentionedwhen each of the subsequent compounds is discussed.

1. Limited signal-to-noise. Individual 1-h data pointsoften display substantial scatter that may be par-tially due to limited signal-to-noise ratio of themeasurements. Some measurement errors may beunderestimated for cases with low signal-to-noise.However, in multiple cases systematic differencesare observed that are not due to random noise.Similarly, differences beyond the uncertainties areoften observed in the diurnal cycles. Thus limitedsignal-to-noise cannot explain all of the observeddifferences. Also, in most cases the signals beingdiscussed are fairly large.

2. Inlet and/or instrument adsorption/desorption ofgases. We estimate the fraction of gas-phase speciespenetrating into each instrument without contact-ing the walls (assuming laminar flow and agas-phase diffusion coefficient of 5£10¡6 m2 s¡1,estimated with SPARC for C5H10O5 (Hilal et al.2003, 2007; Krechmer et al. 2015; Knopf et al.2015) as 10% (39%), 41%, 38%, and 61% forA-CIMS (A-CIMS with shorter inlet, see below), I-CIMS, SV-TAG, and PTR-MS. These estimatesneglect the effect of tubing bends as well as surfacesin the instruments themselves, so it appears thatthere is significant potential for gas/wall interac-tions to play a role in the measurements. Sucheffects could lead to reduced concentrations ormemory effects, which could differ by instrument.For all instruments lower gas concentrations wouldbe expected due to inlet adsorption, while for theSV-TAG and PTRMS an incomplete capture ofgases by the denuders could result in the oppositeeffect, although these inlet effects are not wellunderstood; e.g., there is much recent evidence forwall loss of semivolatile species to PTFE Teflonwalls in chambers (Matsunaga and Ziemann 2010;Zhang et al. 2014a; La et al. 2015; Yeh and Zie-mann 2015; Krechmer et al. 2016), but similareffects for PTFE Teflon inlets have not yet beenstudied in detail. To test for denuder breakthroughin the SV-TAG samples are collected with both a

denuder and a filter inline. Any measured signal isassumed to be incomplete capture by the denuder.This is observed to be negligible for all compoundsdiscussed here. To study such inlet effects in the A-CIMS, the instrument was reconfigured for 10 dayssuch that the gas phase inlet was approximately1/12 of the original length (1/2 m vs. 6 m). No sig-nificant difference was observed in Fp (Figure S5),suggesting the lack of a large effect of the PTFETeflon inlet on gas-phase measurements. However,Krechmer et al. (2016) have reported a significanteffect of adsorption for semivolatile gases for thePTFE Teflon inlet plus IMR combination used inboth CIMS instruments in this study. This leavesthe possibility that a short inlet still had an effectsignificant enough that a larger discrepancy wouldnot be quantified by the comparison of the twoinlet configurations, or that most of the effect isrelated to the ionization region surfaces. Continua-tion of studies to characterize the response of eachfield inlet/instrument source combination to thespecies of interest is highly recommended.

3. Differences in the particle size ranges of the instru-ments. All instruments sampled different particlesize ranges. Upper size cuts were 2.5, 2, 1, and2 mm for A-CIMS, I-CIMS, SV-TAG, and PTR-MS, respectively. The lower size cut of the PTR-MS was 70 nm, compared with »20 nm for theother instruments. In addition devices with thesame nominal size cut can have different detailedtransmission curves. Although all instrumentswere sampling the accumulation mode wheremost of the organic material was present, the dif-ferent size ranges may have led to some differen-ces for the particle measurements. Standardizationof the size ranges sampled and the size cut devicesis recommended for future intercomparisons.

4. Differences in the methods used to quantify instru-ment backgrounds. While backgrounds and testswere incorporated into the analysis conducted for allinstruments to correct for inlet issues, the detailedmethods were different for each instrument. Theeffects are complex and often depend on the specificspecies. Standardization of background methods,and/or intercomparison of the methods, when possi-ble, are recommended for future intercomparisons.

5. Errors in high-resolution fitting of overlapping iongroups. The CIMS/PTRMS measurements involveextracting the intensity of an individual ion in agroup of several overlapping ions. The presence ofthe overlapping ions increases the uncertainty ofthe quantification (Cubison and Jimenez 2015). Inaddition, there may be more ions present in a


group than can be unequivocally determined fromthe data (Stark et al. 2015b). These effects likely dif-fer for each instrument.

6. Differences in the species included in each measure-ment due to instrument sensitivities (in the absence ofthermal decomposition). The CIMS/PTRMS instru-ments measured the sum of all isomers with the sameelemental composition, thus it is possible that themeasurements include isomers other than the onetargeted. IMS data shown below suggests that differ-ent isomers almost certainly play a role in some com-parisons. Different species will likely have differenttime series and Fp, which could result in some of theobserved differences. Note that species with the sameelemental composition can have C� that differ by sev-eral orders-of-magnitude (Krechmer et al. 2015) andthat kinetic processes impacting Fp may depend onstructural details like functional groups. It is also pos-sible that the CIMS/PTRMS instruments measuredthe same isomers, but with different relative sensitivi-ties to each isomer, which would result in differencesin the total concentration measurement time series,Fp, etc. Ion clustering could result in this type of biasas well, but it is thought to be minor or negligible forthe CIMS/PTRMS instruments as used in this study.For the SV-TAG instrument, this type of effect is lesslikely, since the combination of retention time andmass spectral match generally allows for the separa-tion and identification of different chemical isomers.

7. Differences in negative thermal decomposition (orion fragmentation) artifacts. A negative artifact ispossible for all instruments, in which the species ofinterest partially decomposes to smaller species.Thermal decomposition of oxygenated speciesupon heating is a well-known but complex process(Moldoveanu 2009; Canagaratna et al. 2015) andcan result in reduced signal levels when the speciesof interest partially decomposes to smaller frag-ments. The instruments have significant differencesin their thermal desorption profiles (Figure 1). Inparticular, the SV-TAG exposes the particle-phasecompounds to chromatographic analysis and valvetransfers involving temperatures higher thanneeded for purely thermal desorption. The SV-TAG also heats the gas-phase species during analy-sis, while this is not the case for either CIMS.Because of the GC analysis step, the SV-TAG alsosubjects the compounds to two heating cycles com-pared to one for the CIMS. The PTRMS usesheated transfer lines and subjects all gas and parti-cle species to 200�C temperatures. Thus all instru-ments may suffer from thermal decomposition,although the CIMS instruments have the ability to

resolve this effect to some extent. The SV-TAGand PTRMS may be more sensitive to thermaldecomposition due to the higher temperaturesused, although residence time and surface materialsalso play a role, so it is not possible to reach firmconclusions. This effect would lead to artificiallylower Fp for the CIMS instruments, as these instru-ments use heat for the particles but not the gases.The use of authentic standards that are exposed tothe same desorption processes for calibrations forSV-TAG minimizes the impact of these negativeartifacts for the calibrated species, though itremains a possibility that they have some influence.It may also play a role for the many species that arenot calibrated for in the SV-TAG. This effect maycancel out for Fp for the TAG and PTRMS sincethe temperature profiles undergone by gases andparticles are similar, but would lead to overalllower species concentrations. Differences in frag-mentation of molecular ions of the species of inter-est could produce a similar difference. Someevidence of partial thermal decomposition duringthe heating cycles of calibration compounds isshown in Figure S6.

8. Differences in positive thermal decomposition arti-facts. A positive artifact is also possible, in whichthe particle-phase signal of a species is artificiallyincreased due to the decomposition of larger mole-cules (e.g., oligomers including the species of inter-est as a monomer, and/or other thermaldecomposition products). This would lead to alarger measured Fp than would be expected giventhe measured species composition and volatility, ashas been demonstrated for products of isoprenechemistry at this site by Lopez-Hilfiker et al. (2016)and for a large variety of biogenic products mea-sured at this site by SV-TAG (Isaacman-VanWertzet al. 2016). Some insight into this thermal decom-position issue can be obtained from the CIMS ther-mograms, as discussed below. As above, differencesin ion fragmentation of molecules that produce thespecies of interest could produce a similar differ-ence. Finally, this effect could also play a role forthe gas-phase measurements of SV-TAG and PTR-MS due to the exposure to higher temperatures,although this is less likely than for particle-phasemeasurements as species such as oligomers areexpected to be predominantly in the particle-phase.

3.1.4. Additional evidence for the pinonic acid caseTo further investigate the discrepancies mentioned above,two additional layers of data for the CIMS measurementswere investigated. Figure 5 shows the high resolution peak


Figure 5. Additional evidence for the formula C10H16O3 (pinonic acid and isomers, measured in the A-CIMS as C10H15O3¡ and in the I-CIMS as

C10H16O3I¡). (a) High resolution signal fits from the A-CIMS from a one-day averaged mass spectrum. Formula under study is in bold. (b) Signal

vs. temperature for the average ambient heating cycle during SOAS from the A-CIMS and the I-CIMS as the FIGAERO filter is slowly heated (seetext for details). Averages were taken over the 10 days of the study to encompass a range of days and time of day. Variability (1s) shown in errorbars for A-CIMS and shading for I-CIMS. A calibration thermogram within a mixture of 50 compounds is shown. Calibrations carried out using amixture of 50 compounds of varying volatility, but the ambient aerosol will have a different composition and also contains substantial fractionsof inorganic species that were not present in the calibration mixture. The amount deposited was kept close to ambient amounts. (c) High resolu-tion signal fits from the I-CIMS from a one-day averagedmass spectrum. Formula under study is in bold.

Table 2. Summary of key possibilities that can lead to discrepancies in the species intercompared for each instrument. This table isadapted from the list in Section 3.1.3 (number from that list is in parentheses) and includes only possibilities for which data is availableto suggest evidence for or against a given hypothesis. The table also includes our best estimate for the impact that each issue wouldhave on Fp, artificially raising (") or lowering (#) the measured value in relation to the true one, or having an unclear effect (?). Shadingindicates the most likely causes of error (red) vs. lower probability or impact (yellow) and minor errors (green).

Acid Mass Spectral Fitting or Match (#5) Evidence for Multiple Isomers (#6) Evidence for Thermal Decomposition(#7-8)

A-CIMSPinonic Acid Minor peak, Fp ? Yes, Fp " Yes, Fp "Pinic Acid Minor peak, Fp ? Yes, Fp ? Yes, Fp "Hydroxy Glutaric Acid Major peak (minor error) No NoDecanoic Acid Minor peak, Fp? No evidence for or against Yes, Fp "

I-CIMSPinonic Acid Minor peak, Fp? Yes, Fp " Yes, Fp "Pinic Acid Minor peak, Fp ? Yes, Fp ? Yes, Fp "Hydroxy Glutaric Acid Major peak (minor error) Yes, Fp# No

SV-TAGPinonic Acid Good mass spectral match (minor error) N/A No evidence for or againstPinic Acid Good mass spectral match (minor error) N/A No evidence for or againstHydroxy Glutaric Acid Good mass spectral match (minor error) N/A No evidence for or against

PTRMSDecanoic Acid Major peak (minor error) Yes, Fp " Yes, Fp "


fitting used to identify this elemental formula in theA-CIMS and I-CIMS. The peak under study (C10H15O3

¡)is part of a larger group of peaks, increasing signal uncer-tainty. However, the peak separation is large enough thatthe error estimated to arise from peak fitting isonly »15%(Cubison and Jimenez 2015), which is significantly smallerthan the observed differences between the instruments.The situation for the I-CIMS (Figure 5c) is similar. Cubi-son and Jimenez (2015) estimated the fitting uncertaintiesbased on computational experiments. It is possible that fit-ting uncertainties in real measurements are larger, andthese should be the subject of additional study (includingexperiments to better quantify uncertainties).

Figure 5b shows average thermal desorption profilesfrom both CIMS instruments for this elemental

composition, which are quite similar. Also shown is anexample calibration profile of pinonic acid (acquiredwith the A-CIMS), conducted by deposition of a knownamount of pinonic acid (dissolved in a mixture of differ-ent calibration compounds to simulate ambient condi-tions, see figure caption for details) onto the FIGAEROfilter. The calibration compound desorbs from the filterat much lower temperatures (»60�C), than most of theambient signal for both CIMS. It is possible that theambient signal is not dominated by pinonic acid, buteither an isomer with a lower vapor pressure (whichwould cause it to desorb from the filter at higher temper-atures), or the breakdown of a larger molecule or oligo-mer detected at this elemental formula. It is also possiblethat the difference is due to different matrix effects

Figure 6. “Pinic Acid.” Time series (left), scatter plots (center) and diurnal cycles (right) of the measured gas/particle partitioning (Fp toprow), particle phase (2nd row), gas phase (3rd row) and total concentration (bottom row) for A-CIMS, I-CIMS, and SV-TAG. Error bars rep-resent instrumental uncertainties as described in Section 2.5. Fp values modeled with absorptive partitioning are also shown in the toprow. Regressions lines are fixed through the origin and computed using the orthogonal distance regression method (ODR).


between the simple calibration mixture and the complexambient aerosol. However, laboratory testing of differentmatrix effects has not shown a shift as large as observedhere, so this possibility appears unlikely. These complexaspects of the detection process, which will also presentthemselves in different ways in the other instruments,make it difficult to reach a firm conclusion for the rea-sons of the observed differences. A summary of theresults of the comparison and possible reasons for thesedifferences (Table 2) suggest interference from isomersand thermal decomposition for this species. The instru-ments do agree that pinonic acid is almost entirely in thegas phase, and these differences are minor for Fp.

3.1.5. Detailed comparisons for pinic acidThe partitioning values for C9H14O4 (“pinic acid”) indi-cate some level of agreement in the time series, shown inFigure 6, with regression slopes of 0.7 (r2 D 0.31) for I-CIMS/A-CIMS and 1.75 (r2 D 0.38) for SV-TAG/A-

CIMS. For the SV-TAG and A-CIMS 79% of the pointsare within a factor of two of each other and for the I-CIMSand A-CIMS 52% of the points are within a factor of twoof each other, showing some level of agreement. The aver-age diurnal cycles of Fp show a similar temporal trend andmagnitude both between the instruments and between theinstruments and the absorptive partitioning model, withhigher Fp at night and lower during the day. The diurnaltrend of the model is more similar to the A-CIMS and I-CIMS (showing a slight offset). The prediction of parti-tioning to the aqueous phase suggests a median value of1%, so it is considered negligible. The A-CIMS and SV-TAG instruments show more diurnal variation in the par-ticle and gas concentrations, while the I-CIMS changesless throughout the day. The gas-phase diurnal cyclesshow an anti-correlation between the SV-TAG and A-CIMS which also manifests itself in the total measurement(with somewhat lesser variation), with the A-CIMS mea-suring higher gas-phase concentrations than the SV-TAG,

Figure 7. Additional evidence for “pinic acid.” (a) High resolution signal fits from the A-CIMS from a one-day averaged mass spectrum.Formula under study is in bold. (b) Two 1 h averaged ion mobility spectra, taken with an ion mobility nitrate chemical ionization massspectrometer. (c) High resolution signal fits from the I-CIMS from a one-day averaged mass spectrum. Formula under study is in bold.(d) Signal vs. temperature for an averaged ambient heating cycle during SOAS from the A-CIMS and the I-CIMS as the FIGAERO filter isslowly heated (see text for details). Averages were taken over the 10 days of the study to encompass a range of days and time of day.Variation shown in error bars for A-CIMS and shading for I-CIMS. A calibration thermogram with a mixture of 50 compounds is shown.See Figure 4 for more calibration details.


and peaking in the afternoon while the SV-TAG peaks atnight (and the I-CIMS staying fairly constant throughoutthe day). These complex differences might be due to someof the reasons discussed in Section 3.1.3. In particularthere is evidence that the presence of multiple isomersmay be playing an important role, as discussed next.

In Figure 7, additional CIMS and ion mobility nitrateIMS-TOF information is shown for this elemental for-mula. Figure 7a shows that this A-CIMS ion peak(C9H13O4

¡) is also part of a larger group of ion peaks,adding an estimated »10% uncertainty to the quantifica-tion of its area (Cubison and Jimenez 2015), significantlysmaller than the observed differences. Figure 7c shows thesituation for the I-CIMS, where the ion of interest is »1/4rather than »1/2 of the most intense peak in the group.

Figure 7b shows two averaged ion mobility spectra for theelemental formula C9H14O4

¡, during day and night. Oneof the isomers is present in the day and absent at night,and the other isomer is present at night and not duringthe day. Note that the IMS usedNO3

¡ reagent ions and assuch it may detect a different combination of isomerscompared to the A-CIMS and I-CIMS. However, it doesconfirm that there are multiple isomers present and thatthey have variable relative concentrations during the day/night. In Figure 7c, the average ambient thermal desorp-tion profiles are very similar for A-CIMS and I-CIMS, andshow a range of peak desorption temperatures (that showno day/night difference in desorption temperature) withthe calibration compound approximately in the middle, incontrast to pinonic acid. The larger desorption profile

Figure 8. “Hydroxy Glutaric Acid.” Time series (left), scatter plots (center) and diurnal cycles (right) of the measured gas/particle partitioning(Fp, top row), particle phase (2nd row), gas phase (3rd row), and total concentration (bottom row) for A-CIMS, I-CIMS, and SV-TAG. Error barsrepresent instrumental uncertainties as described in Section 2.5. Fp values modeled with absorptive partitioning are also shown in the toprow. Regressions lines are fixed through the origin and computed using the orthogonal distance regression method (ODR).


width in the ambient data is likely due to the presence ofseveral isomers at this molecular formula that have differ-ent volatilities, and/or to thermal decomposition of largermolecules, or to different evaporation matrix effects forthe calibration vs. ambient aerosol.

3.1.6. Detailed comparisons for hydroxy glutaric acidThe comparison for C5H8O5 (“hydroxy glutaric acid”),shown in Figure 8, shows similar issues to those of theother two examples. All instruments show two consistentresults: (1) the diurnal cycles show a lack of variation overthe course of the day, and (2) they all measure high parti-cle-phase fractions. For the SV-TAG and A-CIMS 79% ofthe 1-h datapoints are within a factor of two of each otherand for the I-CIMS and A-CIMS 62% of the points arewithin a factor of two of each other. The organic absorp-tive partitioning model predicts high partitioning values

that do not vary throughout the day as well. The medianprediction of partitioning to the aqueous phase is 8%.Thus partitioning is likely to be dominated by the organicphase, although there is some uncertainty in this conclu-sion given the uncertainty in species thermodynamicproperties. The diurnal trend for the organic vs. waterpartitioning models is very similar (Figure S7) as trendsof temperature and aerosol liquid water content arestrongly correlated, so the two mechanisms cannot be dif-ferentiated based on measured diurnal cycles. It should benoted here that none of the traces from either instrumentshows a correlation with inorganics measured at the site.The gas phase shows more consistent concentrationsamong the three instruments, while the I-CIMS reportshigher particle concentrations.

The high resolution mass spectral fitting for thiselemental formula (Figure 9a) shows thatC5H7O5

¡ is

Figure 9. Additional evidence for the formula C5H8O5 (hydroxy glutaric acid and isomers). (a) High resolution signal fits from theA-CIMS from a one-day averaged mass spectrum. Formula under study is in bold. (b) Two 1 h averaged ion mobility spectra,taken with an ion mobility nitrate chemical ionization mass spectrometer. (c) High resolution signal fits from the I-CIMS from aone-day averaged mass spectrum. Formula under study is in bold. (d) Signal vs. temperature for an averaged ambient heatingcycle during SOAS from the A-CIMS and the I-CIMS as the FIGAERO filter is slowly heated (see text for details). Averages takenover the 10 days of the study to encompass a range of days and time of day. Variation shown in error bars for A-CIMS andblue shading for I-CIMS. A calibration thermogram with a mixture of 50 compounds is shown. See Figure 4 for more calibrationdetails.


the main peak at m/z 147 for A-CIMS, making thepeak-fitting uncertainty a very small source of error(

differences can also be due to model uncertainties, atleast in part. No such correction can be performed forthe SV-TAG instrument as no thermograms arerecorded from the initial particle thermal desorption.SV-TAG peak identification and gas chromatographicidentification information is shown in Figure S8.

3.2. Comparison of A-CIMS and PTRMS

For the comparison between A-CIMS and PTRMS, 3 ele-mental formulas were chosen based on a match of the ele-mental formulas assigned to high-resolution peaks in the

mass spectra of both instruments. Alkanoic acids(CXH2YO2) are compounds for which there are very fewpossible isomers with the same elemental formula but dif-ferent functional group composition, due to their low dou-ble bond equivalency (DBE) of one. The compounds varyin their level of agreement. The comparison of the aver-aged Fp values for each of the species is shown as a scatterplot in Figure 11a. Fp measured by the PTRMS are, onaverage, about half of the A-CIMS values (slope D 0.38,r2D 0.96). Concentrations are of the same order. The larg-est difference is observed for the particle-phase concentra-tions (Figure 11b), as the PTRMS consistently measured

Figure 11. Scatter plots of the average measured Fp for PTRMS vs. A-CIMS. Each point is an average of all measurements for each com-pound for (a) Fp (b) particle-phase concentration, (c) gas-phase concentration, and (d) total concentration over the entire measurementoverlap period from PTRMS vs. A-CIMS. Error bars represent estimated instrumental uncertainties as described in Section 2.5. Regres-sions lines are fixed through the origin and computed using the orthogonal distance regression method (ODR). Regression lines for theSV-TAG and I-CIMS vs. A-CIMS are shown as dotted lines for reference.


lower values, while there is more similarity in the gas-phase and total measurements (Figure 11c and d). Thepossible reasons for these differences are the same onesdiscussed in Section 3.1.3. The heated lines (200�C) usedin the PTRMS inlet system expose all compounds to hightemperatures and could cause some compounds to ther-mally decompose or be produced by thermal decomposi-tion of larger molecules. By comparison, the A-CIMS onlyexposes the compounds to the temperature needed toevaporate them from the FIGAERO filter, and the bulk ofthe measured compounds in this study desorb in the range70–120�C. However, since both the gas and aerosol usethe heated transfer lines in the PTRMS, decompositionmay affect them in the same way, and should not lead to apreferential low bias for the particle phase. In addition, the

two instruments could be measuring isomers with differ-ent functional groups, and thus different vapor pressuresand partitioning fractions. Given that most of the A-CIMScompounds measured are alkanoic acids, there are only afew other possible isomers for the PTRMS. One is ahydroxycarbonyl instead of a carboxylic acid group, butaccording to structure-activity relationships (Pankow andAsher 2008) that would not change the estimated vaporpressure of the molecule by a substantial amount. The Fpvalues show poor agreement with model values (Figure 3),with substantially larger measurement values (consistentwith their C� values shown in Figure S9). This may be dueto thermal decomposition of larger molecules into themeasured molecules, or to the measurement of differentisomers as previously discussed. Previous studies

Figure 12. Time series (left), scatter plot (center), and diurnal cycle (right) of the measured gas/particle partitioning (Fp), particle phase,gas phase, and total concentration for C10H20O2 (“decanoic acid”) for the A-CIMS and PTRMS. The C

� and DHvap used for the absorptivepartitioning model are from Nannoolal et al. (2008) and Chattopadhyay and Ziemann (2005), respectively. Error bars represent estimatedinstrumental uncertainties as described in Section 2.5. Regressions lines are fixed through the origin and computed using the orthogo-nal distance regression method (ODR).


(Yatavelli et al. 2014b) have also observed this measure-ment of much higher Fp values compared to the organicabsorptive partitioning model for alkanoic acids at lowcarbon numbers in a previous study as well as duringSOAS (Figure S10). There is some evidence for this effectin the thermal desorption profiles discussed later.

An example time series of Fp along with gas and parti-cle phase concentrations can be found in Figure 12 for“decanoic acid.” The partitioning values agree on orderof magnitude but not in diurnal trend, and both meas-urements are much higher than the modeled organic par-titioning values. As discussed above, for the average datafor all compounds, the largest difference in concentrationis observed for the particle phase, which is lower in thePTRMS than in the A-CIMS. The gas-phase concentra-tions are more similar, although neither are well corre-lated. Figure 13a shows the average high-resolution fitsfor the A-CIMS. Decanoic acid is a small signal at thisone, but it is well-separated at the rightmost end of theion group, about 2.5 half-widths to the right, and the esti-mated intensity error due to fitting is small. The high-

resolution fits for the PTRMS are shown in 13c, andwhile the main peak is not completely fit the Decanoicacid peak is well separated and well fit. The thermaldesorption profiles for the A-CIMS and the PTRMS areshown in Figure 13b. For the thermograms, the A-CIMSshows lower peak desorption temperatures (»125�C),while the PTRMS shows much higher peak desorptiontemperatures (»200�C). There is no thermogram datafrom calibrations for either instrument, but similar com-pounds tested in previous work suggest that a compoundof this volatility desorb early for the A-CIMS (under100�C). This suggests that the particle phase signal fromboth instruments may be at least partially due to thermaldecomposition of larger molecules, as discussed above.

4. Conclusions

We have presented the first intercomparison of in-situnear real-time measurements of gas/particle partitioning,a very recently developed capability. We use measure-ments from the SOAS field campaign in the southeastern

Figure 13. Additional evidence for C10H20O2. (a) High-resolution peak for the A-CIMS. (b) Average particle-phase thermograms from theA-CIMS, showing field data vs. filter temperature as the FIGAERO filter is slowly heated (see text for details). PTRMS signal vs. tempera-ture as the I-CTD cell is heated is shown. No calibration is available for this compound. (c) High-resolution peak fit for the PTRMS.


United States in the summer of 2013. Although the indi-vidual time series of partitioning fractions show a sub-stantial amount of scatter, the three acids comparedbetween the A-CIMS, I-CIMS, and the SV-TAG span therange of possible Fp values and show better agreementwhen averaged into campaign-long averages. They alsofollow the trend expected from the organic absorptivepartitioning model, with Fp increasing with decreasingspecies volatility, while partitioning to the aqueous phaseis shown to have little/no effect in these cases. The fourthinstrument, the PTRMS, showed the same general trendbut overall lower aerosol partitioning values than the A-CIMS, with both instruments measuring much higherpartitioning values than predicted by the organic absorp-tive partitioning model for those species.

The intercomparison exercise shows agreement ofthe average trends and agreement of Fp values oftenwithin a factor of 2, where the model values can havesubstantial uncertainties due to the uncertain vaporpressures. It is unclear whether the model/measurementdifferences are due to instrumental uncertainty oruncertainty in the model estimation, and it is likely acombination of the two. Better agreement in the meas-urements is needed before stronger conclusions can bemade. Thus there is promise for this new measurementcapability, as well as more confidence in the measure-ment of low vs. high particle-phase fractions. Howevermultiple discrepancies are observed that lie beyond theestimated instrument uncertainties. We suggest that theobserved discrepancies could be due to at least eight dif-ferent effects: (1) Underestimation of measurementerrors under low signal-to-noise conditions; (2) inletand/or instrument adsorption/desorption of gases; (3)differences in particle size ranges sampled; (4) differen-ces in the methods used to quantify instrument back-grounds; (5) errors in high-resolution fitting ofoverlapping ion groups; (6) differences in the isomersincluded in each measurement due to different instru-ment sensitivities; and differences in (7) negative or (8)positive thermal decomposition (or ion fragmentation)artifacts. The available data is insufficient to conclu-sively identify the reasons for the discrepancies, but evi-dence from inlet tests with the A-CIMS, thermaldesorption profiles of CIMS and PTRMS, laboratorydecomposition studies, and IMS-TOF spectra suggestseffects 6–8 as playing an important role. Some of theother effects 1–5 may also contribute in some cases.When positive thermal decomposition artifacts areapproximately corrected for the agreement between theinstruments improves. These comparisons do notclearly indicate that one instrument is performing “bet-ter” or “worse” than the others, and further studies areneeded to clarify the remaining disagreements.

We recommend performing additional laboratory andfield calibrations and experiments aiming to isolate factors1–8 (or other possible effects not listed here) for each of theinstruments.We also recommend additional laboratory andfield intercomparisons. It is worth noting that several of theinstruments discussed here have been improved since theSOAS 2013 data was collected. For example, the FIGAEROinlet has been modified to improve the heating and coolingapparatus and introduce more reliable background meas-urements. The SV-TAG has been improved to reduce thenoise in the data and lower the error associated with mea-surement. Our results show the importance and value ofusing all available data fromCIMS and PTRMS instruments(e.g., HR fits, thermograms). Finally, there is a pressing needfor separation techniques (such as the IMS-TOF or GC-CIMS) that can be interfaced to the CIMS instruments toallow better separation of structural isomers, and a need tobetter understand the role of thermal decomposition in allof these instruments as it related to the measurement of gas/particle partitioning.

Acknowledgments

We are grateful to Annmarie Carlton, Karsten Baumann, andEric Edgerton for their organization of the Centreville Super-site. We thank Julie Phillips of JILA for writing and editingsupport.

Funding

This research was partially supported by NSF AGS-1243354 andAGS-1360834, DOE (BER/ASR) DE-SC0011105 and DE-SC0016559, DOE SBIR DE-SC0011218, and NOAANA13OAR4310063. DOE SBIR award DE-SC0004577 supportedthe development of the FIGAERO-CIMS instrument. SLT andJEK are grateful for CIRES Graduate Fellowships. JEK acknowl-edges an EPA STAR Graduate Fellowship (FP-91770901-0). EPAhas not reviewed this manuscript and no endorsement should beinferred. GIV was supported by NSF Graduate Research Fellow-ship (#DGE 1106400), and the SV-TAG measurements and theUCB/Aerosol Dynamics team were funded by NSF AtmosphericChemistry Program #1250569.

ORCID

Jordan E. Krechmer http://orcid.org/0000-0003-3642-0659Douglas R. Worsnop http://orcid.org/0000-0002-8928-8017Jose L. Jimenez http://orcid.org/0000-0001-6203-1847

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Field intercomparison of the gas/particle partitioning of … · 2018. 11. 30. · CONTACT Jose L. Jimenez [email protected] Department of Chemistry and Biochemistry, and