Gas to Particle Partitioning of Organic Acids in the Boreal Atmosphere

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Gas to Particle Partitioning of Organic Acids in the Boreal Atmosphere Anna Lutz, Claudia Mohr, Michael Le Breton, Felipe D LopezHilfiker, Michael Priestley, Joel Thornton, and Mattias Hallquist ACS Earth Space Chem., Just Accepted Manuscript • DOI: 10.1021/ acsearthspacechem.9b00041 • Publication Date (Web): 03 May 2019 Downloaded from http://pubs.acs.org on May 8, 2019

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ACS Earth and Space Chemistry

Gas to Particle Partitioning of Organic Acids in the Boreal Atmosphere Anna Lutz1, Claudia Mohr2, Michael Le Breton1‡, Felipe D. Lopez-Hilfiker3†, Michael Priestley1, Joel A. Thornton3 and Mattias Hallquist1* 1

Department of Chemistry and Molecular Biology, University of Gothenburg, Gothenburg, 41296, Sweden. 2

Department of Environmental Science and Analytical Chemistry, Stockholm University,

Stockholm, 11418, Sweden. 3

Department of Atmospheric Sciences, University of Washington, Seattle, WA 98195, USA.

*Corresponding authors: Mattias Hallquist ([email protected]) ‡ Current address at Volvo Trucks, Gothenburg, Sweden †Current address at TofWerk AG, Thun, Switzerland Keywords: mass spectrometry, secondary organic aerosol, rural forest, gas-particle partitioning, desorption Abstract Gas to particle partitioning of carboxylic acids was investigated using a High-Resolution Chemical Ionization Time of Flight Mass Spectrometer (HR-CI-ToF-MS) with the Filter Inlet for Gases and AEROsol (FIGAERO). Specifically, the partitioning coefficients of 640 components with unique molecular composition were calculated from an assumed linear relationship between [particle]/[gas] versus the mass of the organic fraction (Morg) according to Raoult’s law, i.e. equilibrium phase partitioning. We demonstrate that using the full data set most of the compounds do not follow a linear relationship. This is especially the case for low and high molecular species. Using a subset of the data, with concurrent low sulfate ambient observations ([SO42- < 0.4 µg m3 ), the relationship improved significantly and a 𝐾 could be derived from the slope of a linear regression to the data. The 100 species with the highest R2 (≥0.7) of this regression are presented. The restrictions during high sulfate conditions can be explained by either changes in the equilibrium conditions (e.g. the activity coeffient, 𝛾 ), or in uptake kinetics (mass transfer limitation). This study demonstrates that partitioning of compounds in the complex ambient atmosphere do follow the ideal Raoult’s law for some limited conditions and stress the need for studies also in more polluted environments.

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1 Introduction Experimental data on equilibrium gas to particle partitioning remains a key limiting factor for modeling descriptions of secondary organic aerosol (SOA), an important atmospheric constituent affecting aerosol particles´ health and climate effects.1, 2 A central property in describing the partitioning between the gas and particle phase is the saturation vapor pressure of the compounds. This has inspired numerous laboratory studies providing vapor pressure data and studies with application of various estimation methods.3 Still, the uncertainties regarding partitioning coefficients and vapor pressures are huge, with estimates of vapor pressures for individual compounds often differing by orders of magnitudes.4 The use of partitioning in atmospheric models is further complicated by the large number of compounds emitted and their subsequent atmospheric oxidation5, leading to the formation of tens to hundreds of first generation oxidation products, which will subsequently undergo further oxidation and transformation.6, 7 However, a promising new framework is utilizing HR-CI-ToF-MS (High-Resolution Chemical Ionization Time of Flight Mass Spectrometer) observations of components with unique molecular compositions in combination with vapor pressure estimation methods.4, 8, 9 Furthermore, the addition of the FIGAERO (Filter Inlet for Gas and AEROsol) inlet to the HR-CI-ToF-MS10-13 combined with the estimation methods provides the potential to create a larger confidence in saturation vapor pressure modeling input. Regarding the equilibrium phase partitioning, i.e. how compounds are distributed between the gas and the particle phase, one often applies Raoult’s Law as described by Pankow et al.,14: 𝐾

∗

Eq.1

∗

where 𝐾 is the partitioning coefficient, Morg the aerosol organic mass concentration, [i]particle and [i]gas the concentration of compound i in the particle and gas phase, respectively, 𝑝 the saturation vapor pressure, 𝛾 the activity coefficient, 𝑀𝑊 the mean molecular mass of the particle constituents, R the gas constant, and T the temperature. The activity coefficient, γ, accounts for deviations from ideal behavior for a compound in a mixture of chemical substances. An alternative way to express partitioning is to use the fraction of a given species (i) in the particle phase (𝐹 , ). This fraction depends on atmospheric conditions and primarily on the amount of organic mass, 𝐹,

1

∗

.

Eq. 2

The use of 𝐹 , comparison of methods used and its interpretation in complex ambient environments, are extensively discussed and outlined together with several challenges by Thompson et al.15 A major difference to the use of 𝐾 is that 𝐹 , is derived for each data point, at a specific Morg, while 𝐾 is assumed to be a constant over a range of Morg. Obviously, they are interlinked and can be traced back to the Raoult’s Law but for the current study we focus on the use of 𝐾 as it can be derived directly from the slope of [i]particle/[i]gas versus measured organic aerosol concentration (Morg). Using the FIGAERO inlet coupled to an HR-CI-ToF-MS provides simultaneous gas and particle phase measurements of numerous compounds, i.e. [i]particle and [i]gas. Thus, it has the potential to both give real time measurements on partitioning and an estimate of the saturation vapor pressure using the thermal desorption profile of individual compounds. The thermal


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desorption profile is derived during a heating cycle from measurements of evaporating particlephase compounds off a Teflon filter. The temperature at which the desorption signal for a component reaches a maximum (Tmax) scales with its saturation vapor pressure.10, 11 An open question for some time has been under what, if any, conditions gas to particle partitioning can be described as being in equilibrium, or if there are any mass transfer limitations .16-18 Gas to particle partitioning includes several steps such as diffusion of the gas to the particle surface, reactions on the surface, diffusion in and out of the bulk and reactions in the bulk. Each of those steps can be rate-limiting and could affect the partitioning as well as the inferred saturation vapor pressure distribution of particle-phase components.19 In several cases the condensed phase matrix are the cause of mass-transfer limitations by, for example, changes in viscosity13, 20, 21 or formation of oligomers.19 The extent of such kinetic limitations depends on both the matrix and the partitioning compounds, where a huge variability must be considered. In this paper we have experimentally investigated how organic acids partition between the gas and the particle phase. Primarily, partitioning coefficients for 640 components with unique molecular compositions were derived using a FIGAERO coupled to a HR-ToF-CIMS10 and evaluated together with their corresponding Tmax. 2 Materials and Methods 2.1 FIGAERO- HR-CI-ToF-MS A filter inlet for sampling gases and aerosol particles(FIGAERO)- connected to a highresolution chemical ionization time-of-flight mass spectrometer (HR-CI-ToF-MS) using acetate ionization was used to measure oxygenated organic compounds (CxHyOz) in a boreal forest in Hyytiälä, Finland, during 24 April to 6 May 2013.8, 10 Briefly, the FIGAERO operates in two modes: 1) sampling of the gas phase and simultaneous collection of particles on a filter, and 2) desorption of particles from the filter with heated ultra-high purity (UHP) N2, and direct introduction of the desorbed compounds into the ionization region of the mass spectrometer. The gas phase was measured with 1Hz resolution for 30 minutes sampling using a PTFE inlet (16 mm inner diameter (ID), length 320 cm), at 22 standard liters per minute (slpm). At the same time particles were collected on a filter (Zefluor 25 mm, pore size 1 μm, Pall) by sampling using a stainless steel inlet (ID 20 mm, length 315 cm), at ~23.5 slpm. The high flow rates gave residence times of a few seconds limiting e.g. gas line adsorption.22 To analyse the particle composition the particles were desorbed thermally for 45 min by flowing UHP N2 over the filter. The desorption temperature was controlled at a ramp rate of 10 °C min-1 until reaching 200 °C with a soak at that temperature for the remaining time. The main ionization mechanism for acetate is proton abstraction, see reaction (1): RCOOH + CH3C(O)O- → RC(O)O- + CH3C(O)OH

(1)

Whether the abstraction is successful or not is determined by the gas phase acidity of the compound relative to acetic acid. Acetic acid has a low gas-phase acidity compared to organic acids in the atmosphere, making its conjugate base an effective ion to detect acids.e.g 8, 23-25. The ions measured represent components with unique molecular compositions, but can have different structures (be isomers), or, for the desorption phase, also result from fragmentation of larger compounds (thermal decomposition at the elevated temperatures.10, 12, 19 The use of acetate ionization with its selectivity towards carboxylic acids, and desorption profile analyses allow us to largely constrain these



effects. Still, in our analysis of partitioning properties of specific compounds, we cannot entirely rule out potential interference of thermal decomposition or isomers. 2.2 Particle phase desorption The temperature where a compound exhibits a maximum in signal (ion counts) is commonly referred to as that compound´s Tmax and is correlated with the enthalpy of sublimation.10 As has been reported earlier by Lopez-Hilfiker et al.25, it is common that a single desorption of a compound with a specific molecular composition can have more than one maximum in signal, see Figure S1. This can be caused by, e.g. the presence of isomers with different saturation vapor pressure, or fragments from thermally decomposed higher molecular weight compounds.12, 19, 26. Therefore, all desorption profiles for each molecular composition were analyzed with a custom nonlinear least squares peak fitting routine. The first step was to identify desorption profiles with only one maximum (i.e. Tmax) to obtain a single-compound representative desorption peak shape. The other desorption profiles were fitted with an iterative Levenberg–Marquardt algorithm for nonlinear least squares problems using a variable number of one compound representative peak shapes. The number of desorption peaks, the location and the amplitude of each peak were optimized to fit the total shape of the desorption profile, and used to retrieve Tmax and the concentration for each peak. Based on pure compound desorption profiles, the peak shape can vary up to 30%,25 therefore the standardized peak shape was allowed to vary with the same percentage. To avoid over fitting, a maximum of three standardized peak shapes were allowed for each desorption. In the cases where more than one Tmax was obtained, only the first desorption peak was selected and its corresponding integrated ion counts was used for partitioning calculations. The second desorption peaks typically had a Tmax between 20-100°C higher than the first one, leading to the conclusion that those were thermally decomposed accretion products from larger molecules, rather than isomers25. An example of a desorption peak is shown in Figure S1. During the campaign there were in total 115 desorption cycles analyzed for Tmax. In order for a compound/ion to be presented with a valid Tmax for further analysis, the Tmax needed to be successfully identified in at least 80 of the analyzed desorptions. This was true for 90% of the detected compounds although the majority were detected 100 times or more. The particle backgrounds were corrected for by introducing another filter (Zefluor 25 mm, pore size 1 μm, Pall) before the sampling filter in the particle phase inlet line. This was followed by desorption of the particle sample filter. This background check was automatically conducted every fourth desorption. The backgrounds before and after a sample were then linearly interpolated and used to correct the sample by subtraction. We set the detection limit for each compound to be one standard deviation of all measurement points of each compound, and the limit of quantification to three times the standard deviation. For the gas-phase we applied the method described previously.8 2.3 Gas to particle partitioning For the calculation of the phase partitioning we used the ion counts i for the gas and particle phase corrected for the volumetric sampling. The partitioning coefficient (𝐾 ) for a species i was calculated from the slope of [i]particle/[i]gas versus organic aerosol concentration (Morg) derived from a high-resolution time-of-flight aerosol mass spectrometer (AMS, Aerodyne Research, Inc.) concurrently measuring non refractory PM1 chemical composition with a time resolution of 5 min. It is noted that here the absolute concentrations of measured compounds are not needed to derive 𝐾 due to the ratio between the two being used. However, to generally estimate concentrations and


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total contributions to aerosol mass we converted the ion signal to mass concentrations using a sensitivity of 20 counts s-1 ppt-1.8 By using the maximum sensitivity, i.e. at the derived collisional limit, for all compounds, a lowest limit estimate for the mass concentration is expected. 3 Data More than 800 ions were identified to a mass accuracy of ≤ 20 ppm, 779 of which had high enough intensities to be quantified in both particle and gas phase. The requirement of quantification in both gas and particle phase for the same sampling time period reduced the total number of data points to typically between 30-110 (average number of data points: 88±25,1std). Of all the identified compounds the phase partitioning coefficients and Tmax for 640 acids could be extracted. The data was divided in two subset corresponding to low ([SO42-] < 0.4 µg m-3) and high ([SO42-] > 0.4 µg m-3) concentration of particulate sulfate as measured with the AMS. There are approximately equal number of data points for these two conditions, N(low)=46±15 vs N(high)=40±8 (the range given at 1 std). 4 Results 4.1 Observation of pinic and pinonic acid To generally illustrate the gas and particle phase measurements and their variability over the campaign Figure 1 shows to the time trends of two selected compounds. The two acidic compounds, pinic and pinonic acid, with corresponding molecular composition, are well-known as products from oxidation of monoterpenes and would be expected as major constituents in ambient forest influenced air masses. Their vapor pressures have been measured in the laboratory28, 29 and they have frequently been detected in the atmosphere.30-33 Thus, the molecular species corresponding to C10H16O3 and C9H14O4 are here discussed as “pinonic acid” and “pinic acid”, respectively. Here C10H16O3 (pinonic acid) in the top panel, is present in the gas phase at a much higher relative extent than C9H14O4 (pinic acid), which is predominantly present in the particle phase, see bottom panel. These results are in line with pinic acid having a lower saturation vapor pressure than pinonic acid28 and commensurable with previous partitioning studies.15 Thus, our measurements of these compounds and their relative gas -particle distributions correspond to their relative expected vapor pressure. The total concentration, i.e., the sum of gas and particle phase concentrations, is similar for both compounds. Previously these compounds has been measured in Hyytiälä by Kristensen et al.34 but using off-line analysis of filter/denuder samples. Their measurements were during summertime with higher ambient temperatures, giving higher emission rates from vegetation, and therefore it was not so surprising they observed higher concentration. Regarding the gas to particle partitioning there was a general agreement where both studies measured a considerably higher fraction in the particle phase for pinic acid than for pinonic acid illustrating the effect of the compounds’ different saturation vapor pressure on the partitioning.



Figure 1. Time trends of particle and gas phase concentrations, measured with a FIGAERO- HR-CI-ToFMS with acetate as reagent ion, during the campaign in Hyytiälä 2013. In the top panel is pinonic acid, C10H16O3, which has a higher saturation vapor pressure than pinic acid, C9H14O4, shown at the bottom.

4.2 Evaluation of desorption measurements The median maximum desorption temperature, Tmax, represents a qualitative measure of the saturation vapor pressure for each compound detected in the particle phase 10. However, the Tmax for a certain ion signal might be caused by several isomers 35, fragmentation of larger compounds 12, or be affected by reversible oligomer formation 19. Shown in Figure 2 are our extracted Tmax from the fitting procedure plotted versus the molecular weight (Mw) of each detected ion. The markers are color coded according to the compounds´ O/C ratio. Generally, Tmax increases with Mw and O/C ratio, in line with heavier molecules and more oxygenated molecules having a lower saturation vapor pressure 36 i.e., more heat is required to evaporate them. It is expected that a compound of a high Tmax exists predominantly in the particle phase, i.e., has a higher 𝐾 and vice versa for a low Tmax. Using only the first Tmax for a selected ion reduces the influence of fragmentation, but one may notice that there are several small molecular weight ions with high O/C ratio that have a rather high Tmax and may be fragmentation products. Still the majority of ions follows the expected trend in Tmax with Mw and O/C. Furthermore, several previous studies have used measurements of Tmax to estimate individual compounds’ saturation vapor pressures,10, 11 thus it could be a suitable property to evaluate our efforts to derive partitioning coefficients from ambient data.


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Figure 2. Median Tmax derived from more than 80 thermograms versus molecular weight for all compounds detected with acetate FIGAERO-CIMS. If more than one local Tmax was found (more than one desorption maximum) only the Tmax with the lowest temperature is displayed assuming any Tmax at higher temperatures are from fragmentation.

4.3 Partitioning coefficients of organic acids For 640 constituents with unique molecular composition measured in Hyytiälä the 𝐾 were derived, i.e. the slope resulting from a linear fit of the ion signal ratio in particle and gas phase, plotted versus total organic mass observed during the campaign. Figure 3 shows this for C5H9O3-.



Figure 3. Ratio in particle and gas phase signal of the ion C5H9O3- versus total organic mass (Morg) for all observations during the campaign. According to Eq.1 the slope of a linear fit will provide 𝐾 for the selected constituent.

Since Tmax is scaling with decreasing saturation vapor pressures one would assume, from equation 1, that 𝐾 for all compounds would increase with their Tmax (proxy for lower saturation vapor pressures). This was not found and, if anything, there was a negative correlation (see Figure S2). Consequently, in the following we will explore different reasons for this apparent discrepancies between expected and observed behavior. As a starting point one can note that the 𝐾 is derived from the slope using data points covering the entire period of the measurement campaign. The quality of the regression providing the slope can be evaluated statistically. Here, the quality of this slope was evaluated using the coefficient of determination (R2) for the linear fit to the data. Figure 4 shows the R2 from the linear fit according to equation (1) versus median Tmax, using all data and for conditions with either low or high particulate sulfate [SO42-], i.e. using a threshold of [SO42-] = 0.4µg/m3) as described below.


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Figure 4. Coefficient of determination lumped into bins with ± 5 °C for all data and for a subset representing occasions when SO42- in the particle phase was lower than 0.4 µg m-3, and occasions when SO42- in the particle phase was higher than 0.4 µg m-3. For each box, the middle line gives the median. The bottom and top edges of the box are the 25th and 75th percentiles (q1 and q3), respectively. The whiskers (w) represents the most extreme data points not considered an outlier, defined as ±2.7σ (99.3 percent coverage if the data are normally distributed). Outliers are denoted with '+' and is defined as greater than q3 + w × (q3 – q1) or less than q1 – w × (q3 – q1).

For the full data-set R2 is generally very low, and there is a clear trend of R2 being considerably lower for constituents with smaller and larger Tmax. The reason for this observation was investigated further by selecting subsets of the data covering various ambient conditions, e.g. particle size distributions, relative humidity, temperature, sulfate, nitrate, and ammonium content in PM2.5. Most striking from this analysis was that during occasions when the particle concentrations of sulfate were low ([SO42-] µg m3 ), Figure 4, shows a similar pattern as R2 for all the data. We thus conclude that the partitioning is not following equation 1 when the sulfate concentration is high, especially not for compounds with high and low Tmax, i.e. higher and lower volatility compounds. For the low SO42- data-set the partitioning does follow equation 1, and we could derive 𝐾 for 640 species ranging in values from 0.04 to 5.5 µg-1m3, Figure 5. Within this subset also the 𝐾 increased with increasing Tmax, in line with the assumption that higher Tmax scales with decreasing saturation vapor pressures shifting the partitioning towards the particulate phase, Figure 5. Generally, the derived 𝐾 for the low SO42data-set was higher than corresponding data for the high SO42- data-set (Figure S2). Since the high SO42- data set had lower R2 these data are only shown in supplemental. For the 100 species with the highest R2 within the low SO42- data set the derived 𝐾 values are given in the supplemental (Table S1). For these 100 species the p-value for the linear fit was always less than 0.01 providing high statistical confidence. The effects on partitioning for high sulfate conditions could have



several reasons where the most obvious is that the partitioning, for each compound, has not reached equilibrium37 or that the activity coefficient (𝛾 ) depends on other factors, e.g., molecular size.38

Figure 5. 𝐾 derived for subsets representing occasions when SO42- in the particle phase was lower than 0.4µg/m3.

To further investigate this and illustrate the effect on 𝐾 of the low and high sulfate air masses, Figure 6 presents the [i]particle/[i]gas versus Morg for four selected compounds with different Ki. (a box-plot with corresponding 𝐾 is shown in Figure S3) A feature is that the data points, especially for the high Mw compounds, derived during high SO42- (open circles) are below the fitted line for the low SO42- occasions. From a gas phase concentration reference point the particle phase for those points has a lower concentration than expected from equilibrium, assuming the real 𝐾 are according to the fitted line. Two major explanations for this could be 1) the condensed phase has a different composition making the activity coefficient (𝛾 ) larger for the high SO42cases. 2) There is a kinetic limitation due to e.g. higher viscosity, thus providing reduced mass transfer to the bulk of the particles for particles in air masses containing high levels of SO42-.


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Figure 6. Examples of particle to gas ratios plotted against organic mass. Filled circles denote data points where the SO42- concentration was lower than 0.4 µg m-3. Unfilled circles denote data points where the SO42- concentration was higher than 0.4 µg m-3. The solid line describes the linear fit to the data where the SO42- concentration was lower than 0.4 µg m-3 and the dashed line describes the linear fit where the SO42concentration was higher than 0.4 µg m-3. The two selected dimers (C16H21O6- and C19H25O7-) have been identified by Mohr et al, 2017.

5 Discussions Acetate ionization has a profound selectivity for acidic compounds partly restricting our conclusions to effects on organic acid partitioning. It has been postulated that the sulfate content could influence partitioning of dicarboxylic acids 39, related to humidity or acidity effects.40 Since we could not resolve any effects of humidity or ammonium content it is less likely that it is the sulfate itself that changes the properties of the aerosol, but rather conditions such as ageing, interaction with anthropogenic pollutants or cloud processes that affect either the equilibrium



conditions (e.g. 𝛾 ), or the uptake kinetics (mass transfer limitation). The activity coefficient of a specific compound depends on the interaction with the absorbing aerosol particle mixture. For selected dicarboxylic acids it has been demonstrated that there can be a significant molecular size dependence where larger acids have larger 𝛾 38. Indeed, for the high sulfate selection, 𝐾 is generally smaller for larger compounds, Figure 5. However, one may note that the activity coefficients would also affect mass transfer.37 Although this will support the effect caused by changes in activity coefficient one may also get a dependence on molecular properties (e.g. effective saturation vapor pressure) if there is a mass transfer limitation.16 For mass transfer limitations both surface properties and aerosol particle viscosity may restrict the uptake 21, 41. The effects as illustrated by the four compounds in Figure 6 could thus be caused by gas-phase production and reduced uptake on high SO42- containing particles. In the Amazon the effect of anthropogenic influence increasing the measured rebound fraction (during impaction) of particles was shown to be a proxy of particle viscosity.42 Obviously, the Amazon and rural Finland are different environments but unfortunately no rebound measurements were available at the time of our measurements even if it has been demonstrated that aerosols in Hyytiälä in general could be viscous.20 Chemical induced oxidation either in gas or particle phase (e.g. in cloud processing) could enhance the sulfate content and in parallel provide less volatile/highly viscous aerosol particles. However, the O:C measured by the AMS gave very similar average values for high (0.61) and low (0.60) SO4-2 conditions. We also investigated a potential connection between the occurrence of nucleation (frequent during the period of our study), and periods with high or low SO4 (Figure S4). There was no obvious link to nucleation events, and high sulfate loads were mostly found after nucleation events, where more significant aerosol mass was produced. A back-trajectory analysis illustrating the transition from a high to a low sulfate episode indicated air masses originating from more polluted areas when high sulfate was observed, pointing towards the importance of anthropogenic ageing (Figure S5). For selected species the derived Fp , from the data obtained during low SO42- conditions, versus Morg are shown in the supplemental (Figure S6, Table S2, S3). As expected, the derived 𝐹 , , depends on the actual organic mass and the data are generally following equation 2. There are agreements with some previous literature15, 43 but the difficulty of this measurement and dependence on Morg makes comparison complex.15 Another difference to 𝐾 is that 𝐹 , is derived for each data point, a specific Morg, while 𝐾 can be derived as the slope of a linear regression with associated statistical uncertainties, here using R2. In the study of Isaacman-VanWertz et al.43 they in addition to 𝐹 , also present derived effective saturation concentration constant (𝐶 ) for selective compounds that easily can be converted to 𝐾 (𝐶 = 1/𝐾 ). Generally, our study provides much higher 𝐾 for the masses corresponding to the compounds in that study. For the five of the selected compounds illustrated in the supplemental information we derive 𝐾 between 1 and 5 µg m-3 while Isaacman-VanWertz et al.43, derive values between 0.1 and 0.5 µg m-3. Knowing the different assumptions and uncertainties for the methods, where their study was done in the much warmer southeastern United States and central Amazonia, the absolute comparison was fair. In addition, both studies agree on a much lower 𝐾 for Pinonic acid compared to the other five compounds. Generally, the FIGAERO method has been scrutinized in various ways.e.g. 10-12, 44 The two major concerns have been fragmentation during the desorption cycle and that the unique molecular identification cannot resolve isomers. The acetate ionization method is selective towards acids and


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will thus reduce the possibilities for multiple non- acidic isomers. The impact of fragmentation on the derived partitioning coefficients would be either contributions of fragments, from a higherweight molecular compound, to the particle phase signal and thus increase Ki, or (partial) dissociation of the evaporated component giving a lower particle phase signal and Ki. Neither of these effects are expected to depend on the air mass origin or the sulfate content of the aerosol. Generally, the comparison of 𝐾 to derived Tmax could, in addition, be obscured by other factors such as the formation of reversible oligomers as reported by D’Ambro et al.19. Interestingly, this would be in favour of an air-mass effect but we could not detect any differences in Tmax between the low vs high sulfate subset and the effect would rather enhance the particle phase signal providing higher 𝐾 than expected from the linear relationship, see Equation I, while we observe lower 𝐾 , see Fig 4. However, a very recent study highlights the role of particle-phase processing, and the underestimated importance of sulfate aerosol for monoterpene-SOA formation.45 It could thus be a plausible explanation that there is a sulpate induced decomposition process in the condensed phase that deplete the particle phase and cause a shift in the gas to particle partitioning.

6 Conclusion In conclusion, the partitioning of the 640 compounds measured as ions with different molecular weight were influenced by aerosol composition where a linear relationship between [particle]/[gas] versus Morg holds for the majority of the compounds under low sulfate conditions while at high sulfate condition there is a significant scatter. The scatter is mainly caused by more data points with lower [particle]/[gas] and the effect is enhanced at high molecular mass. The effect can be explained by changes in either the equilibrium conditions (e.g. 𝛾 ), or the uptake kinetics (mass transfer limitation). The study demonstrates that partitioning of compounds in the complex ambient atmosphere do follow the ideal Raoult’s law for some conditions and challenge new measurements in more polluted environments.

7 Acknowledgments The research presented is a contribution to the Swedish strategic research area ModElling the Regional and Global Earth system, MERGE. This work was supported by the Swedish Research Council (grant numbers 2014-05332) and Formas (grant number 214-2010-1756, 9422015-1537). We thank Liqing Hao and Annele Virtanen for providing AMS sulfate data. 

The authors declare no competing financial or nonfinancial interests.

Supporting Information Available: Figure S1. Example on desorption peak evaluation Figure S2. 𝐾 calculated from the whole campaign versus the corresponding Tmax. Figure S3. Box-plot illustrates the variability of 𝐾 during the measurements campaign. Figure S4. Particle number concentration during the campaign Figure S5. Back trajectory calculation during a change from high to low SO42- conditions. Figure S6 and corresponding description. Partitioning using 𝐹𝑖,𝑝 including comparison with literature



Table S1. Calculated 𝐾𝑖 for the 100 constituents with the highest R2 Table S2. Average 𝐹𝑖,𝑝 for selected compounds for day and night conditions together with some previous measurements. Table S3. Site descriptions for data in Table 2.

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Gas to Particle Partitioning of Organic Acids in the Boreal Atmosphere

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