Impact of Thermal Decomposition on Thermal Desorption Instruments

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Impact of thermal decomposition on thermal desorption instruments: advantage of thermogram analysis for quantifying volatility distributions of organic species Harald Stark, Reddy L. N. Yatavelli, Samantha L. Thompson, Hyungu Kang, Jordan Edward Krechmer, Joel R. Kimmel, Brett B Palm, Weiwei Hu, Patrick L. Hayes, Douglas A. Day, Pedro Campuzano-Jost, Manjula R. Canagaratna, John T. Jayne, Douglas R. Worsnop, and Jose L. Jimenez Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.7b00160 • Publication Date (Web): 23 Jun 2017 Downloaded from http://pubs.acs.org on June 27, 2017

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Environmental Science & Technology

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Impact of thermal decomposition on thermal desorption instruments: advantage of thermogram analysis for quantifying volatility distributions of organic species

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Harald Stark,1,2,3* Reddy L. N. Yatavelli,1,3,+ Samantha L. Thompson,1,3 Hyungu Kang, 1,3 Jordan E. Krechmer,1,3 Joel R. Kimmel,2,4 Brett B. Palm,1,3 Weiwei Hu,1,3 Patrick L. Hayes,5 Douglas A. Day, 1,3 Pedro Campuzano-Jost, 1,3 Manjula R. Canagaratna, 2 John T. Jayne,2 Douglas R. Worsnop,2,6 Jose L. Jimenez1,3*

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[1] Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, CO, 80309 USA [2] Aerodyne Research Inc., Billerica, MA, 01821 USA [3] Department of Chemistry, University of Colorado, Boulder, CO, 80309 USA [4] TOFWERK AG., 3600 Thun, Switzerland [5] Department of Chemistry, Université de Montréal, Montréal, QC, H3T 1J4, Canada [6] Department of Physics, University of Helsinki, Helsinki 00014, Finland [+] Now at Monitoring and Laboratory Division, California Air Resources Board, El Monte, CA, 91731 USA * To whom correspondence should be addressed: email: [email protected], phone: 303-492-0820; fax: 303-492-1149 [email protected], phone: 303-492-3557; fax: 303-492-1149

TOC Cover art:

Abstract We present results from a high-resolution chemical ionization time-of-flight mass spectrometer (HRToFCIMS), operated with two different thermal desorption inlets, designed to characterize the gas and aerosol composition. Data from two field campaigns at forested sites are shown. Particle volatility distributions are estimated using three different methods: thermograms, elemental formulas, and measured partitioning. Thermogram-based results are consistent with those from an aerosol mass spectrometer (AMS) with a thermal denuder, implying that thermal desorption is reproducible across very different experimental setups. Estimated volatilities from the detected elemental formulas are much higher than from thermograms since many of the detected species are thermal decomposition products rather than actual SOA molecules. We show that up to 65% of citric acid decomposes substantially in the FIGAERO-CIMS, with ~20% of its mass detected as gas-phase CO2, CO, and H2O. Once thermal decomposition effects on the detected formulas are taken into account, formula-derived volatilities can be reconciled with the thermogram method. The volatility distribution estimated from partitioning measurements is very narrow, likely due to signal-to-noise limits in the measurements. Our findings indicate that many commonly used thermal desorption methods might lead to inaccurate results when estimating volatilities from observed ion formulas found in SOA. The volatility distributions from the thermogram method are likely the closest to the real distributions. 1 ACS Paragon Plus Environment


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1 Introduction

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Secondary organic aerosol (SOA) plays an important role in atmospheric processes and impacts climate forcing1 and human health2. Because SOA is formed by chemical reactions in the atmosphere, its formation and composition needs to be understood for impact mitigation.3–6 In addition, it is also important to measure the SOA volatility in order to quantify the fraction of the chemical compounds that will remain in the particle phase versus partition actively between the gas and particle phases, as impacts and atmospheric lifetime change drastically between phases.

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Thermal desorption mass spectrometry (TDMS) techniques have been used to quantify the volatility of organic aerosols over the past 15 years. Chattopadhyay and Ziemann7,8 quantified vapor pressures of individual mono- and dicarboxylic acids by depositing pure aerosolized substances on a solid vaporizer followed by temperature-programmed thermal desorption (TD) and detection by electron ionization mass spectrometry. The same study7 also showed that different vaporizer materials can result in differences in the TD profiles.

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Several frameworks for classifying OA in terms of volatility and bulk chemical characteristics have been developed over the last decade, ranging from one-dimensional volatility distributions,9 to multidimensional distributions by adding a second, chemical dimension.6,10–13 Several studies have demonstrated methods to quantify bulk OA volatility distributions based on calibration of the TD temperatures against saturation concentrations. TDMS analyses have been accomplished both inside the instrument14 and using a thermal denuder inlet14–17. However, the mass spectrometric detector used in most of those studies, the Aerosol Mass Spectrometer (AMS),18,19 suffers from extensive thermal decomposition and ion fragmentation due to the high vaporizer temperatures (~600°C) and electron ionization (70 eV, much larger than bond strengths in molecules). Thermal decomposition of oxidized compounds has been observed at vaporizer temperatures as low as 200 °C, the lowest temperature required to vaporize OA,20,21 which does not allow for molecular identification of OA species.

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Due to these chemical analysis limitations, several other MS instruments have been developed to quantify OA chemical composition. Many of these use chemical ionization MS (CIMS), which is relatively “soft”, often retaining the original molecules after ionization and broadly sensitive to a large range of these compounds.22–24 Combined with high mass-to-charge ratio (m/z) resolution, this method allows assigning elemental formulas to ions directly related to atmospheric compounds.25 When combined with TD methods, these techniques allow more detailed simultaneous quantification of both volatility and chemical composition. An alternative technique to perform OA chemical analysis and estimate OA volatility distributions is thermal desorption-gas chromatography.26,27

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Recently, several advances in TD inlet design have allowed near-simultaneous chemical characterization of the ambient gas phase and particle phase compounds.28–33 These inlets, when combined with timeof-flight mass spectrometers (ToF-CIMS), provide information on a very broad range of compounds.34 High mass resolution is available in many instruments and allows more precise constraints on the detected species via their accurate mass.35 The estimation of bulk OA volatility distributions from TD data from these techniques, using the method of Faulhaber et al14 has recently been demonstrated.36 It

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is also possible to estimate species volatilities from the measured elemental formulas, assuming a functional group composition.37–39

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Since several of the TDMS instruments just described can measure the gas and particle phases (nearly) simultaneously, real-time measurements of gas-particle partitioning of individual species have recently been demonstrated.26,29,40,41 Species volatility can also be derived from the measured gas-particle partitioning using partitioning theory.42 A recent study43 compared gas-particle partitioning field measurements from four different collocated TDMS instruments. Some of the measured compounds agreed well on average, but substantial differences were observed in many cases that exceeded the stated uncertainties. A potentially important cause of some of the discrepancies may be differences in thermal decomposition during TD analysis across the different instruments.

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In summary, three methods can be utilized to estimate bulk and species volatility distributions from the new TD-CIMS and similar instruments from: 1) calibrated TD desorption profiles vs temperature; 2) the measured elemental formulas, assuming a functional group composition; and 3) the measured partitioning between the gas and particle phases. The three methods to estimate OA volatility distributions have not been compared, to our knowledge

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In this study, data from two field campaigns at forested sites are analyzed using two slightly different TDMS techniques, MOVI-CIMS and FIGAERO-CIMS. Bulk OA volatility distributions are estimated for both datasets using the three different methods. The results are compared with those from independent measurements using thermal denuder-AMS. The impact of species thermal decomposition in these inlets is demonstrated and discussed in terms of reconciling the results from the different methods. Finally, the implications of our results for atmospheric research are discussed.

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2 Experimental methods

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2.1 Field Campaigns The Bio-hydro-atmosphere interactions of Energy, Aerosols, Carbon, H2O, Organics & Nitrogen Rocky Mountain Biogenic Aerosol Study (BEACHON-ROMBAS) took place in July and August, 2011 at the Manitou Experimental Forest Observatory site in a Ponderosa pine forest in central Colorado44. The Southern Oxidant and Aerosol Study (SOAS) was a large-scale field campaign that was part of the Southern Atmosphere Study (SAS). The data in this study were acquired at the main SOAS Supersite located in a mixed forest of deciduous and evergreen trees in Centreville, Alabama during June and July 2013.45,46 Both sites had strong impacts from biogenic emissions, and varying levels of anthropogenic pollution.

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The micro-orifice volatilization impactor (MOVI) used in this study used a stainless steel post coated with an inert material (SilicoNert 2000, Restek Corp., Bellefonte, PA, USA) as an impactor to collect fine particles, while simultaneously analyzing the gas phase. Particles were thermally desorbed at regular intervals by slowly increasing the post temperature while flowing N2. During BEACHON-RoMBAS the

2.2 Instrumentation



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MOVI was operated with a desorption time of 8 minutes and an overall time resolution of 1.5 h. Further details of this technique28 and its application during this study29,47 can be found elsewhere.

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The filter inlet for gases and aerosols (FIGAERO) uses a Teflon filter for particle collection concurrently with gas-phase analysis. Particles were analyzed every 55 minutes by thermal desorption as in the MOVI, but with longer desorption time periods of 30 minutes. Also, heated N2 was used for desorption instead of heating the post for the MOVI. The FIGAERO was developed to improve upon limitations of the MOVI, in particular allowing the use of separate inlets for gas and particle sampling, and attempting to minimize artifacts from gas phase compounds adsorbing to the collection surface. Further details of this technique30 and its application and slight modifications for the SOAS 2013 study43 are given elsewhere .

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Both studies used the same detection system: a high-resolution chemical ionization time-of-flight mass spectrometer (HRToF-CIMS, hereinafter “CIMS”),25,34 which allows separation and identification of isobaric ion signals. Acetate was used as the reagent ion, which is very sensitive to organic acids via proton abstraction and not sensitive to most other compounds due to the thermodynamics of the proton transfer.48 Recent studies have shown that clustering with the acetate ion can also be important.49 In these studies the ion optics were adjusted to minimize detection of ions resulting from clustering reactions (“strong declustering mode”). Therefore, most detected ions resulted from a formal deprotonation reaction, even though clusters may have been important in the initial ionization. CIMS data were analyzed with the Tofware package (v.2.5.6, Tofwerk, Switzerland and Aerodyne, USA)35 within the Igor Pro software (v.6.37, Wavemetrics, USA). Two lists of ~1400 and 1800 possible ions were developed to fit to all high-resolution spectra acquired during BEACHON and SOAS, respectively.35 Sensitivities for all molecular formulas were estimated from an empirical fit to calibration data, as a function of carbon number and oxidation state.50

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We also used data from an Aerodyne high-resolution time-of-flight aerosol mass spectrometer (HR-ToFAMS, hereinafter “AMS”,19 coupled to a thermal denuder to estimate OA volatility distributions14,15, which was deployed in both campaigns.46,51

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2.3 Estimation of volatility distributions Here we quantify species volatility using the saturation mass concentration at a reference temperature ∗ of 25 °C ( ). The saturation concentration is expressed in units of µg m-3. The vapor pressure (pv) of a ∗ species is related to its via 9:

×

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∗ = ×

Equation 1

where MW is the species molecular weight and R is the universal gas constant. We use the word “species” from here on to refer to all compounds that are detected as ions with a common elemental formula, and which may include multiple isomers.43 The species formulas are derived by adding the proton (H+) abstracted during ionization. OA volatility distributions were estimated in three different ways, described below.


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2.3.1 Thermogram Method: Estimation of species ∗ from desorption temperatures ∗ is estimated directly from the desorption temperature (Td), after calibration with In this method, known species or mixtures.7,14,36 A thermogram is recorded by linearly increasing Td of the aerosol collection substrate (i.e. post for MOVI and filter for FIGAERO) while monitoring mass spectral signals.

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∗ ∗ To create vs Td calibration curves for our studies, multiple organic acids with known were dissolved together in a solvent (isopropanol) at low concentrations, and manually deposited on the MOVI post or FIGAERO filter with a syringe, and then thermally desorbed using the same Td profile used for atmospheric sampling. The isopropanol solvent is expected to evaporate at low desorption temperatures and not affect the desorption profiles. Figure 1 shows calibration signals versus Td. Each species’ thermogram can be summarized by one Td value, e.g. that corresponding to the maximum signal, or the Td at which 50% of the signal has been desorbed. In this study we use the first method, and both methods are compared in Figure S1(Supp. Info.). Fits to empirical functions were used, as they allowed determination of precise peak temperatures. Average “desorption functions” were calculated for each method for use as basis functions, see Figures S5 and S7 as well as detailed description in the supporting information. While the basis functions were developed from the calibrations, the fitting routine was not intended to extract individual species concentrations from a sum thermogram. The idea is that the basis functions are used to fit the sum thermograms for the field data with a “volatility basis ∗ ) spacing between them), since there are multiple species present set approach” (with constant log( and their identities are only partially constrained. Differences in the chemical environments do not appear to influence the general shape of the thermograms (Fig S7), but rather result in different C*298 ∗ distributions (e.g. due to activity coefficient changes). The calibration for the entire temperature ∗ range is achieved by linear regression of individual values as a function of inverse temperature (see ∗ Figure 2A). The deviations of the fits to the calibration data are small and result in uncertainties in ∗ of less than 1 order of magnitude. The quantification of has increasing uncertainty due to extrapolation for values below 10-2 μg m-3. This figure also shows a comparison to similar calibrations for different instrumental setups. The large differences in both slopes and intercepts highlight the importance of calibrations for each individual instrument, as effects such as temperature measurement location, wall materials and desorption technique can have a strong effect.

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“Bulk organic acid” thermograms, shown in Figure 2B and C, were calculated by converting all organic signals to mass concentrations using a sensitivity function derived in a previous study50 and summing them. Volatility distributions were derived by fitting the bulk ambient thermograms with multiple basis ∗ functions centered at regular intervals,36 also described in more detail in the supporting information.

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2.3.2 Formula Method: Estimation of species ∗ from elemental formulas In this method, the formulas of the identified ions in the high-resolution mass spectra, together with assumptions about functional group composition, can be used in a vapor pressure estimation method.37– 39 A volatility distribution can then be derived by summing the estimated species concentrations into ∗ regularly-spaced bins. We used the SIMPOL group contribution method to calculate volatilities from the compound formulas.38 Average errors in SIMPOL are thought to be ~1 order-of-magnitude.37 5 ACS Paragon Plus Environment


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This method has been shown to perform well for estimating volatilities of 16 important products of αPinene oxidation.52 Since acetate is most effective for the detection of organic acids, we assumed that at least one carboxylic acid group was present in each detected molecule. The remaining oxygen in the measured formulas was assumed to be in one of three main functional groups: carbonyl (C=O), carboxyl (C(O)-OH), or hydroxyl (OH), as these are very common functional groups in biogenic SOA. We estimated volatilities assuming all remaining oxygen was in one of those functional groups, which resulted in limiting cases for the volatilities, highest for carbonyl, lowest for hydroxyl, and intermediate for carboxyl.

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2.3.3 Partitioning method: Estimation of species ∗ from apparent gas/particle split In this method, the separate measurements of gas-phase and aerosol concentrations can be combined to calculate particle-phase fractions for each species (Fp), which, combined with known OA mass concentration and ambient temperature, can be used to estimate C*. Fp is calculated from the measurements as:

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Equation 2

where P and G are the signals or mass concentrations for a given species in the particle and gas phases, respectively. C* can be estimated by applying partitioning theory as:9

∗ × 1 ×

Equation 3

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where OA is the organic aerosol concentration (measured by AMS, µg m-3). The concentrations of different species can then be summed to produce a bulk volatility distribution as in the formula method. Partitioning and, therefore, saturation concentration are temperature-dependent. The average ambient temperatures and standard deviations for the two studies were 18±7 °C and 25±3 °C for BEACHON and SOAS, respectively. A temperature change of about 10-15 °C is needed to achieve a change of 1 decade in C*, given typical vaporization enthalpies.53 Therefore, the temperature changes during these two studies were small enough to not have a significant influence on the volatility distributions, compared to the uncertainties of the different methods and especially their differences. Also, these narrow temperature ranges justify the use of the reference temperature of 298 K for C*.

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3 Results and discussion

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3.1 Thermogram fits Thermogram fits for both measurement campaigns are shown in Figure 2. The average thermogram from BEACHON (Figure 2B) showed substantial signal remaining at high temperatures, which is captured by the basis function. No additional corrections were needed to achieve a quantitative fit. The observed high temperature signal could be a result of adsorption on the MOVI post material, which was coated metal. This surface can exhibit stronger adsorption, which results in slower desorption (“tails” in the 6 ACS Paragon Plus Environment

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average thermogram and basis functions) than for the Teflon filter material used in the FIGAERO (Figure 2C). Alternatively, secondary desorption from surfaces other than the post/filter could occur, where compounds can re-condense on colder surfaces during the early phase of the heating cycle and then desorb again once the entire setup warms up. This effect is expected to be stronger on the MOVI due to its larger thermal mass, different material and more complex geometry (impaction surface vs. filter collection), as well as closer location to the ionization region and other surfaces that can get passively heated. Also, the MOVI was held at the highest temperature for shorter time periods than the FIGAERO (1.5 vs. 20 minutes), which may also contribute to a higher remaining signal. It is also possible that lower volatility material was present during BEACHON than SOAS, which cannot be resolved within the limitations of these methods. The total acid aerosol concentration of each campaign equates to about 50% of the bulk AMS OA data, consistent with previous studies.47

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The thermogram fits in Figure 2 can be directly converted to a volatility distribution, shown as blue bars in Figure 3. These results are consistent with expectations and with prior results from similar methods in several field studies.16,36 Only the thermogram method distributions are consistent with those estimated from applying the Faulhaber et al. thermogram method14 to thermodenuder-AMS data, also shown in Figure 3, with BEACHON/MOVI being slightly more consistent than SOAS/FIGAERO. The consistency of ∗ the results is especially good when considering the uncertainty of at least 1 decade14 and the substantial differences between the methods, as well as the fact that the CIMS is only measuring ~½ of the OA mass measured by the AMS. E.g. particles in the MOVI or CIMS were collected on a surface in a layer much deeper than individual particles, while particles in the thermodenuder -AMS were suspended in air while evaporating. TD timescales were also almost 2 orders of magnitude shorter in the thermodenuder -AMS compared to the MOVI and FIGAERO. Thus, the similarity of the results suggests that thermal desorption occurs similarly in very different experimental setups, which means that volatility distributions estimated by the thermogram method for other TD instruments can also expected to be comparable to the ones from the instruments discussed here. The distributions show a major fraction of the OA being effectively non-volatile under atmospheric conditions for both campaigns. Some differences of detail are observed between campaigns, but they do not affect our conclusions. It is out of the scope of this paper to investigate how those differences may result from differences in sources and chemistry between these campaigns.

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Figure 3 also shows the volatility distribution estimated from the formula method based on the ∗ functional group assumption that resulted in the lowest possible for this method (all oxygen in excess of an acid group was present as hydroxyl groups). Lastly, the volatility distribution from the partitioning method is also in Figure 3. Major differences are apparent between the volatility distributions estimated from the three methods. The thermogram method shows ions corresponding to ∗ low-volatility (low ) compounds at significant signal levels and only limited mass (25% and 0.5% for ∗ BEACHON and SOAS, respectively) for species with > 10 µg m-3 that would be expected to reside predominantly in the gas-phase according to partitioning theory. A possible reason for these signals at high volatilities could be adsorption of gas-phase compounds to the MOVI or FIGAERO surfaces, which then desorb at the start of the TD cycle. We performed measurements in which a Teflon filter was

3.2 Comparison of volatility distributions from all methods



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inserted upstream of the post or filter to estimate the amount of gas-phase signal due to this adsorption artifact. Results from these measurements (see Figure S4 in supp. info.) indicate an upper limit of about 20% of adsorbed gas-phase compounds relative to the total particle signal, similar to the high-volatility signal observed in the thermograms. Thermal decomposition at these very low temperatures is unlikely to be the cause of these signals. Even some of the weakest bonds such as peroxide groups only decompose at ~80°C.54 In contrast, the formula method results in volatility distributions dominated by ∗ ∗ much higher . A majority of the signal (80%, for both studies) corresponds to species with > 10 -3 µg m , which would be expected to reside in the gas-phase and thus not be part of the aerosol analyzed here. Errors in SIMPOL cannot explain the extremely large observed differences.37 It could be hypothesized that high volatility species could be trapped inside a “glassy” aerosol.55 However, the aerosol in SOAS was mostly liquid56 and above the humidity levels where significant diffusion limitations are encountered in both studies,29,56 and thus diffusion limitations should not have played a role in the partitioning of ambient aerosol. Thus a very large fraction of very volatile species renders the volatility distribution derived from the formula method implausible. The partitioning method results are very different from those of the other two methods, with narrow distributions that would suggest that almost all of the organic acids are semivolatile. If that was the case, they should evaporate quickly at relatively low temperatures in the instruments or their ambient concentrations should respond very strongly to ambient temperature changes, neither of which is observed.

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∗ The from the partitioning method rely on a ratio between gas phase and particle signals. Detection limits for both of these limit the measurable range of this ratio. Most (95%) of the G/P values were between ratios of 1/16 and 21/1 for SOAS and 1/5 and 160/1 for BEACHON. These ranges only allow a ∗ variation of of 3 and 2.6 orders of magnitude for SOAS and BEACHON, respectively, therefore resulting in narrow distributions from the partitioning method. For example, with an average OA of ∗ around 2 µg m-3, as observed in the BEACHON campaign, derived from equation 3 can only vary between 0.4 and 320 µg m-3, which is close to the range observed for the distributions from the ∗ partitioning method, but much narrower than the range from the other methods. We also note that thermal decomposition will degrade the performance of the partitioning method, although the limited measurement range is likely the most important effect limiting the application of that method. In addition, both particle and gas phase signals are background-subtracted, possibly resulting in negative ∗ values for either. These negative values would result in negative values when calculated from Equation 3, thus disappearing from the volatility distribution altogether. The fractions of total mass concentrations from these outliers were relatively small at 1% and 2% for BEACHON and SOAS, respectively. Overall, the first effect significantly limits the use of partitioning values for determining ∗ . A large disagreement of partitioning and thermal methods for chamber SOA has also been reported by Saha and Grieshop (2016).57

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3.3 Thermal decomposition as the main cause of the differences The most likely explanation for the differences between the thermogram and formula methods is that the molecules detected by the CIMS after TD of the particle phase result from thermal decomposition during the desorption process. This was demonstrated by Lopez-Hilfiker et al.36 for a few specific isoprene oxidation products analyzed with the FIGAERO-CIMS during SOAS. However, the magnitude of 8 ACS Paragon Plus Environment

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∗ this effect (in terms of the change in ) is larger than would be suggested by the results of Lopez20,21 Hilfiker et al. That and other studies have confirmed that highly functionalized molecules can undergo thermal decomposition. However, decomposition would need to be a dominant fate for a large fraction of organic acids present in the aerosol in order to explain the differences observed here. Many compounds present in SOA could show a high degree of fragmentation, because they often contain multiple functional groups58, including peroxide groups which are thermally labile54.This has been shown to be the case for the AMS, at vaporizer temperatures as low as 200 °C,20,21 as well as for the FIGAEROCIMS in a chamber study59. To our knowledge, it has not been reported for the bulk molecular composition of ambient SOA.

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At temperatures as low as 200 °C, many organic acids and alcohols are known to decompose.60 To evaluate the potential magnitude of this effect in the FIGAERO, we performed experiments with citric acid. This species was chosen because Canagaratna et al.20 showed that it was prone to thermal decomposition in the AMS (to CO2, CO, and H2O), at levels comparable to those of other highly oxidized species, even at low vaporizer temperatures of 200 °C. The experimental details are described in the Supporting Information. Results are shown in Figure 4, which clearly demonstrates that about 20% of the mass of citric acid appears in a typical FIGAERO thermogram as CO2(g), CO(g), and H2O(g), with the level of thermal decomposition being about ½ of that observed in the AMS. The AMS subjects the particles immediately to a high temperature (600°C) tungsten surface, and thus it is possible that it leads to more thermal decomposition, and in particular more dehydration than the FIGAERO. The higher fraction of H2O for the AMS data may also be partially due to the likely high ionization efficiency of this species61 in the AMS. We note that this fractional mass decomposition is lower than the fraction of citric acid molecules undergoing decomposition, due to the larger molecular mass of citric acid compared to the decomposition products. I.e., if every citric acid molecule lost an acid group by decarboxylation, that would only represent 33% of the total mass. Thus, the ~20% of citric acid mass appearing in decomposition products in Figure 4 may account for up to 65% of all citric acid molecules undergoing thermal decomposition.

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In addition, Figure S2 shows that the average carbon numbers from the FIGAERO-CIMS and AMS datasets are similar, and smaller than expected for particle-phase molecules. Thus thermal decomposition is likely responsible for most of the discrepancy between the thermogram and the other methods. The small overlap between these distributions indicates further that a large fraction of the detected compounds must be a result of thermal decomposition, which means that many aerosol compounds detected by the acetate CIMS were not actually present in the atmosphere with the chemical formula with which they were detected. Rather, the detected species were mostly a result of chemical transformations during the desorption process. On the other hand, it has been shown that some individual compounds with fewer functional groups that are more thermally stable, such as alkanoic acids or the acids used in the calibration measurements (Figure 1), can be detected via their parent ion.29 Combining these two statements indicates that a large fraction of SOA consists of highly functionalized molecules.



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It should be noted that thermal decomposition will have an effect on the thermograms, as shown in Figure S3 for the citric acid standard. At least in this case the differences between the thermograms of the parent molecule and the decomposition products are minor, especially compared to the differences with the results from the other techniques to estimate the volatility distribution.

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Lastly, it should be pointed out that desorption time scales could have an influence on the extent of thermal decomposition, if both occur at comparable rates. While decomposition rates of multifunctional complex organic compounds are highly uncertain, the fact that the volatility distributions from the thermal denuder, in which the desorption takes place on the order of seconds or less, are consistent with the thermogram method with minute desorption time scales, suggests that decomposition time scales are likely to be short for SOA compounds.

3.4 Can the distributions be reconciled?

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We performed a sensitivity study to evaluate the degree of thermal decomposition that, on average, would be needed to explain the differences between the results of the different methods. Volatility distributions before thermal decomposition can be estimated by assuming that the molecules lost a certain number and type of functional groups during heating, and then applying the formula method to the updated elemental compositions. Here, pre-decomposition distributions were estimated by the simple assumption that the measured formulas had each lost a carboxyl group (-CO2), a carbonyl group (-CO) and a hydroxyl group (-H2O, assuming dehydration involved the loss of a hydroxyl group and formation of a C=C double bond). All these thermal decomposition pathways have been observed in organic acids and alcohols,60 as also shown in the previous section. The modified distributions are shown in Figure 3C-D. A large shift towards lower volatilities results, more consistent with the distributions from the thermogram method. The shift does not result in the exact same relative bin heights due to a redistribution of the shifted individual ion signals into the new bins. We conclude that on average a loss of one or more functional groups for a majority of the detected acids is likely to have occurred. It is also possible that other decomposition mechanisms, such as breaking of C-C bonds, are important as well, as evidenced by the relatively low carbon numbers measured during the analysis of particle species. For example, the fact that formic acid is a large fraction of the particle phase signal indicates that some organic molecules decompose producing this simple organic acid.

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Thermal decomposition could also result in a loss of all carboxyl groups, which would make the product molecule undetectable by the acetate CIMS used in this study. A closure study that would quantify the amount of CO2, CO, H2O, and organic non-acids produced during thermal desorption of ambient samples would be useful to further quantify this phenomenon. The fact that the amount of aerosol detected during both studies was lower than the mass of OA detected by the AMS indicates that it is plausible that some acid molecules may have lost their detectability with the CIMS during thermal desorption.

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3.5 Atmospheric Implications The finding of substantial thermal decomposition of most oxidized organic molecules detected in atmospheric particles at forested sites in TD measurement techniques has several implications. First, only the volatility distributions derived from analysis of thermograms yield results consistent with the available constraints. In the case of the two field studies investigated, organic acid aerosol is of mostly 10 ACS Paragon Plus Environment

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low volatility, with saturation vapor pressures below 1 µg m-3 and with a substantial fraction of ∗ effectively non-volatile species under atmospheric conditions (those with log( ) < 10-2 µg m-3). This is consistent with the limited observed response of particle-phase acids and OA to ambient temperature changes. The distributions derived from the thermogram method could have errors for several reasons, ∗ including: (a) the effect of thermal decomposition, with a significant bias towards higher for oligomer decomposition36,62 and a smaller effect for functional group loss (at least for the citric acid ∗ example); (b) the extrapolation of the calibration to lower values; (c) variations in desorption temperatures or basis function shapes with changing aerosol mixtures. These errors should be further investigated with known mixtures. There is significant evidence from the literature for the importance of (a) for biogenic SOA, while it is not clear if the other effects are important or in which direction they may bias the results. If (a) dominates for ambient SOA, the real volatility distribution of ambient acids may be even lower than shown in this study, further increasing the differences with the other methods of estimating volatility distributions.

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Secondly, most of the signal from instruments deriving oxidized OA molecular composition data from techniques in which the aerosol is heated before or during analysis,8,14,26,30,32 likely does not represent the actual molecular composition. Importantly, the molecular size, oxidation state, and water solubility of the compounds present in the aerosol may be substantially underestimated by such analyses. While extensive decomposition is likely to occur for all types of oxidized OA, some signal may still be retained for some of the molecules present in the aerosol, which can be exploited in data analysis and interpretation. If the decomposed fraction is consistent during calibration and field measurements, the species concentrations could still be derived from TD measurements. Further, decomposition kinetics are temperature dependent, which could allow retaining parent molecules if the TD is performed at lower temperatures over longer time periods, depending on the relative rates of evaporation and decomposition vs. temperature. Since TD instruments are used very widely to quantify aerosol composition and volatility, our findings have important consequences for the atmospheric chemistry community. Further study of thermal decomposition in instruments and its impact on atmospheric measurements, as well as the development of methods to quantify aerosol composition that do not involve heating, are recommended.

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4 Acknowledgements This research was partially supported by grants from DOE (BER/ASR) DE-SC0016559 & DE-SC0011105, and DOE SBIR DE-SC0011218, EPA STAR 83587701-0, EPRI 10004734 and 10007056, and NSF AGS1360834. BBP and JEK acknowledge EPA graduate fellowships (FP-91770901-0 and FP-91761701-0). SLT and JEK acknowledge CIRES fellowships. This manuscript has not been reviewed by EPA, and thus no endorsement should be inferred. PLH acknowledges funding support from Université de Montréal. We would like to thank Paola Massoli and John Nowak for providing data for Figure S3. We would also like to thank the CIMS user community for many valuable discussions.



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5 Supporting information ∗ Alternative methods for calibration, comparison between CIMS particle cycles and AMS during BEACHON, thermogram for citric acid desorption, average thermograms for heating, zero and filter cycles, basis functions used for thermogram fitting, setup of FIGAERO experiments to investigate the thermal decomposition of citric acid, relative shape of calibration thermograms, carbon number distributions for gas and particle phase signals.

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Figure 1: Individual thermograms (markers) for MOVI (A) and FIGAERO (B). Temperature-programmed thermal desorption profiles were recorded at a constant temperature increase rate of 20 °C min-1, for about 8 min for the MOVI (40-200 °C) and 10 min for the FIGAERO (50-250 °C), followed by constant temperature time periods of 1.5 and 20 min at 200 °C (“soak”) for MOVI and FIGAERO, respectively. 18 ACS Paragon Plus Environment

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∗ Figure 2: (A) calibrations between and inverse temperature, including comparison between this and previous studies. Vertical errors bars are estimated as 1 decade in vapor pressure.37 Horizontal error bars are 1 sigma of peak desorption temperatures from concentration series. In this study we used low concentration organic acid mixtures in solvent (isopropanol) of 4-5 acids. The differences between instruments primarily indicate substantial differences in desorption kinetics in the different setups, and also potentially in the relationship between the measured temperature at the location used for calibration for each setup and the actual temperature experienced by the analyte. ; (B) BEACHON and (C) SOAS average thermograms of the sum of all organics with volatility bin fitting on top axis, along with ∗ fits to individual bins and sums thereof. The fits were constrained to only allow variation of heights of basis functions, while positions and shapes were fixed from calibration measurements.


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Figure 3: Comparison of estimated volatility distributions for particle phases from thermogram, formula and partitioning methods, compared with thermal denuder+AMS measurements (A,B); effect of thermal 21 ACS Paragon Plus Environment


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decomposition (loss of CO2, H2O and CO) on formula distribution, compared to thermogram results (C,D).

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Figure 4: (Left) Typical time traces of CO2, CO, and H2O concentrations during FIGAERO thermal desorption of citric acid monohydrate standards. Also shown is an estimate of the thermal decomposition rate estimated from the literature,63 which shows good correspondence with our results. (Right) Fraction of citric acid mass detected as CO2(g), CO(g), and H2O(g) with initial citric acid mass of 1 mg. Also shown is AMS data from a previous study20 with AMS vaporizer temperature at 200 °C.

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Impact of Thermal Decomposition on Thermal Desorption Instruments

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