Gas to Particle Partitioning of Organic Acids in the Boreal Atmosphere

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Article http://pubs.acs.org/journal/aesccq

Cite This: ACS Earth Space Chem. 2019, 3, 1279−1287

Gas to Particle Partitioning of Organic Acids in the Boreal Atmosphere Anna Lutz,† Claudia Mohr,‡ Michael Le Breton,†,§ Felipe D. Lopez-Hilfiker,∥,⊥ Michael Priestley,† Joel A. Thornton,∥ and Mattias Hallquist*,† †

Department of Chemistry and Molecular Biology, University of Gothenburg, 412 96 Gothenburg, Sweden Department of Environmental Science and Analytical Chemistry, Stockholm University, 114 18 Stockholm, Sweden ∥ Department of Atmospheric Sciences, University of Washington, Seattle, Washington 98195, United States Downloaded via 109.94.222.175 on August 13, 2019 at 12:49:16 (UTC). See https://pubs.acs.org/sharingguidelines for options on how to legitimately share published articles.

‡

S Supporting Information *

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-weight species. Using a subset of the data, with concurrent low sulfate ambient observations ([SO42− < 0.4 μg m−3), the relationship improved significantly and Ki 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 changes in either the equilibrium conditions (e.g., the activity coeffient, γi) or uptake kinetics (mass transfer limitation). This study demonstrates that partitioning of compounds in the complex ambient atmosphere follows ideal Raoult’s law for some limited conditions and stresses the need for studies also in more polluted environments. KEYWORDS: mass spectrometry, secondary organic aerosol, rural forest, gas−particle partitioning, desorption HR−CI−ToF−MS10−13 combined with the estimation methods provides the potential to create a larger confidence in saturation vapor pressure modeling input. With regard to the equilibrium phase partitioning, i.e., how compounds are distributed between the gas and particle phase, one often applies Raoult’s law as described by Pankow et al.14

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 health and climate effects of aerosol particles.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 magnitude.4 The use of partitioning in atmospheric models is further complicated by the large number of compounds emitted and their subsequent atmospheric oxidation,5 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 using high-resolution chemical ionization time-of-flight mass spectrometer (HR−CI−ToF−MS) observations of components with unique molecular compositions in combination with vapor pressure estimation methods.4,8,9 Furthermore, the addition of the gas and aerosol filter inlet (FIGAERO) to the © 2019 American Chemical Society

Ki =

[i]particle [i]gas Morg

=

RT MWomγi pi0

(1)

where Ki is the partitioning coefficient, Morg is the aerosol organic mass concentration, [i]particle and [i]gas are the concentrations of compound i in the particle and gas phases, respectively, p0i is the saturation vapor pressure, γi is the activity coefficient, MWom is the mean molecular mass of the particle constituents, R is the gas constant, and T is the temperature. The activity coefficient, γ, accounts for deviations from ideal behavior for a compound in a mixture of chemical substances. Received: Revised: Accepted: Published: 1279

March 1, 2019 April 29, 2019 May 3, 2019 May 3, 2019 DOI: 10.1021/acsearthspacechem.9b00041 ACS Earth Space Chem. 2019, 3, 1279−1287

Article

ACS Earth and Space Chemistry

direct introduction of the desorbed compounds into the ionization region of the mass spectrometer. The gas phase was measured with 1 Hz resolution for 30 min of sampling using a polytetrafluoroethylene (PTFE) inlet (inner diameter (ID), 16 mm; length, 320 cm), at 22 standard liters per minute (slpm). At the same time, particles were collected on a filter (Zefluor 25 mm, with a pore size of 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 analyze 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 3).

An alternative way to express partitioning is to use the fraction of a given species (i) in the particle phase (Fi,p). This fraction depends upon atmospheric conditions and primarily the amount of organic mass. Fi,p =

[i]particle [i]gas + [i]particle

ij C* yzzz = jjjj1 + z j Morg zz k {

−1

(2)

The use of Fi,p, 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 Ki is that Fi,p is derived for each data point, at a specific Morg, while Ki is assumed to be a constant over a range of Morg. Obviously, they are interlinked and can be traced back to Raoult’s law, but for the current study, we focus on the use of Ki because it can be derived directly from the slope of [i]particle/[i]gas versus the measured organic aerosol concentration (Morg). Using the FIGAERO inlet coupled to a 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 desorption profile is derived during a heating cycle from measurements of evaporating particle phase 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 is 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 upon both the matrix and partitioning compounds, where a huge variability must be considered. In this paper, we have experimentally investigated how organic acids partition between the gas and particle phase. Primarily, partitioning coefficients for 640 components with unique molecular compositions were derived using FIGAERO coupled to a HR-CI−ToF−MS10 and evaluated together with their corresponding Tmax.

RCOOH + CH3C(O)O− → RC(O)O− + CH3C(O)OH (3)

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.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 toward 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 Tmax of that compound and is correlated with the enthalpy of sublimation.10 As 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 the signal (see Figure S1 of the Supporting Information). This can be caused by, e.g., the presence of isomers with different saturation vapor pressures or fragments from thermally decomposed higher molecular weight compounds.12,19,26,27 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. On the basis of 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 overfitting, a maximum of three standardized peak shapes were allowed for each desorption. In the cases where more than one 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 high-resolution 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, from April 24 to May 6, 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 ultrahigh-purity (UHP) N2 and 1280

DOI: 10.1021/acsearthspacechem.9b00041 ACS Earth Space Chem. 2019, 3, 1279−1287

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

was obtained, only the first desorption peak was selected and its corresponding integrated ion counts were used for partitioning calculations. The second desorption peaks typically had a Tmax between 20 and 100 °C higher than the first peak, leading to the conclusion that those were thermally decomposed accretion products from larger molecules rather than isomers.25 An example of a desorption peak is shown in Figure S1 of the Supporting Information. During the campaign, there were in total 115 desorption cycles analyzed for Tmax. For a compound/ion to be presented with a valid Tmax for further analysis, 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 was detected 100 times or more. The particle backgrounds were corrected by introducing another filter (Zefluor 25 mm, with a pore size of 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 3 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 (Ki) for a species i was calculated from the slope of [i]particle/[i]gas versus the organic aerosol concentration (Morg) derived from a high-resolution time-offlight 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 Ki as a result of the ratio between the two

being used. However, to generally estimate concentrations and total contributions to aerosol mass, we converted the ion signal to mass concentrations using a sensitivity of 20 counts s−1 ppt−1.8 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 phases. The requirement of quantification in both gas and particle phases for the same sampling time period reduced the total number of data points to typically between 30 and 110 (average number of data points = 88 ± 25, with one standard deviation). Of all of the identified compounds, the phase partitioning coefficients and Tmax for 640 acids could be extracted. The data were divided into two subsets corresponding to low ([SO42−] < 0.4 μg m−3) and high ([SO42−] > 0.4 μg m−3) concentrations of particulate sulfate, as measured with the AMS. There are an approximately equal number of data points for these two conditions, N(low) = 46 ± 15 versus N(high) = 40 ± 8 (the range given at one standard deviation). 4. RESULTS 4.1. Observation of Pinic and Pinonic Acids. To generally illustrate the gas and particle phase measurements and their variability over the campaign, Figure 1 shows the time series of two selected compounds. The two acidic compounds, pinic and pinonic acids, with corresponding molecular composition, are well-known as products from oxidation of monoterpenes and would be expected as major constituents in an ambient forest influenced air masses. Their vapor pressures have been measured in the laboratory,28,29 and they have frequently been detected in the atmosphere.30−33 Thus, the molecular species corresponding to C10H16O3 and C9H14O4 are discussed here as “pinonic acid” and “pinic acid”, 1281


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Figure 2. Median Tmax (°C) derived from more than 80 thermograms versus molecular weight for all compounds detected with acetate FIGAERO−HR−CI−ToF−MS. If more than one local Tmax was found (more than one desorption maximum), only Tmax with the lowest temperature is displayed, assuming that any Tmax at higher temperatures is from fragmentation.

may notice that there are several small molecular weight ions with a high O/C ratio that have a rather high Tmax and may be fragmentation products. Still, the majority of ions follow the expected trend in Tmax with Mw and O/C ratio. Furthermore, several previous studies have used measurements of Tmax to estimate saturation vapor pressures of individual compounds;10,11 thus, it could be a suitable property to evaluate our efforts to derive partitioning coefficients from ambient data. 4.3. Partitioning Coefficients of Organic Acids. For 640 constituents with unique molecular composition measured in Hyytiälä, Ki were derived, i.e., the slope resulting from a linear fit of the ion signal ratio in particle and gas phases, plotted versus total organic mass observed during the campaign. Figure 3 shows this for C5H9O3−. Because Tmax is scaling with decreasing saturation vapor pressures, one would assume, from eq 1, that Ki 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 of the Supporting Information). Consequently, in the following, we will explore different reasons for this apparent discrepancy between expected and observed behavior. As a starting point, one can note that Ki 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 R2 from the linear fit according to eq 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−] = 0.4 μg/m3, as described below. 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

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 the 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 have been measured in Hyytiälä by Kristensen et al.34 but using offline 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 that they observed a higher concentration. With regard to 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 different saturation vapor pressures of compounds on the partitioning. 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, Tmax for a certain ion signal might be caused by several isomers35 or fragmentation of larger compounds12 or be affected by reversible oligomer formation.19 Shown in Figure 2 is 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 O/C ratio of the compounds. 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 Ki and vice versa for a low Tmax. Using only the first Tmax for a selected ion reduces the influence of fragmentation, but one 1282


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set, the partitioning does follow eq 1 and we could derive Ki for 640 species ranging in values from 0.04 to 5.5 m3 μg−1 (Figure 5). Within this subset, also, Ki increased with

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

Figure 3. Ratio in particle and gas phase signal of the ion C5H9O3− versus total organic mass (Morg, μg/m−3) for all observations during the campaign. According to eq 1, the slope of a linear fit will provide Ki for the selected constituent.

increasing Tmax, in line with the assumption that higher Tmax scales with decreasing saturation vapor pressures, shifting the partitioning toward the particulate phase (Figure 5). Generally, derived Ki for the low SO42− data set was higher than corresponding data for the high SO42− data set (Figure S2 of the Supporting Information). Because the high SO42− data set had lower R2, these data are only shown in the Supporting Information. For the 100 species with the highest R2 within the low SO42− data set, the derived Ki values are given in Table S1 of the Supporting Information. 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 (γi) depends upon other factors, e.g., molecular size.38 To further investigate this and illustrate the effect on Ki 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 Ki is shown in Figure S3 of the Supporting Information). A feature is that the

content in PM2.5. Most striking from this analysis was that, during occasions when the particle concentrations of sulfate were low ([SO42−] < 0.4 μg/m3), e.g., an indication of air masses with low anthropogenic influence and likely not very aged, the pattern of correlations changed considerably compared to using the entire data set (see Figure 4). For the low sulfate subset, all R2 improved where the dependence of Tmax on R2 disappeared and also the compounds with lower and higher Tmax had high R2 values. The classification of low versus high sulfate conditions, i.e., a breaking point at 0.4 μg/ m3, was derived as an approximate transition point between a good fit and a poor fit to the partitioning equation. The coefficient of determination for the high SO42− subset ([SO42−] > 0.4 μg m−3) (Figure S2) shows a pattern similar to R2 for all of the data. We thus conclude that the partitioning is not following eq 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

Figure 4. Coefficient of determination lumped into median Tmax (°C) 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% coverage if the data are normally distributed). Outliers are denoted with “+” and are defined as greater than q3 + w(q3 − q1) or less than q1 − w(q3 − q1). 1283


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Figure 6. Examples of particle/gas ratios plotted against organic mass (Morg, μg/m−3). Filled circles denote data points where the particle [SO42−] was lower than 0.4 μg m−3. Open circles denote data points where the [SO42−] was higher than 0.4 μg m−3. The solid line describes the linear fit to the data where the [SO42−] was lower than 0.4 μg m−3, and the dashed line describes the linear fit where the [SO42−] was higher than 0.4 μg m−3. The two selected dimers (C16H21O6− and C19H25O7−) have been identified by Mohr et al.8

the equilibrium conditions (e.g., γi) or the uptake kinetics (mass transfer limitation). The activity coefficient of a specific compound depends upon 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 γi.38 Indeed, for the high sulfate selection, Ki is generally smaller for larger compounds (see Figure S2 and Figure 6). However, one may note that the activity coefficients would also affect mass transfer.37 Although this will support the effect caused by changes in the activity coefficient, one may also obtain a dependence upon 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 was an increase in the measured rebound fraction (during impaction) of particles as 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,

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 that real Ki are according to the fitted line. Two major explanations for this could be as follows: (1) The condensed phase has a different composition, making the activity coefficient (γi) larger for the high SO42− cases. (2) There is a kinetic limitation as a result of, 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−.

5. DISCUSSION 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 Because we could not resolve any effects of humidity or ammonium content, it is less likely that it is sulfate itself that changes the properties of the aerosol but rather conditions such as aging, interaction with anthropogenic pollutants, or cloud processes that affect either 1284


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relationship (see eq 1), while we observe lower Ki (see Figure S2). 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 sulfate-induced decomposition process in the condensed phase that depletes the particle phase and causes a shift in the gas to particle partitioning.

even if it has been demonstrated that aerosols in Hyytiälä in general could be viscous.20 Chemical-induced oxidation in either the 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 ratio measured by the AMS gave very similar average values for high (0.61) and low (0.60) SO42− 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 SO42− (Figure S4 of the Supporting Information). 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 low sulfate episode indicated air masses originating from more polluted areas when high sulfate was observed, pointing toward the importance of anthropogenic ageing (Figure S5 of the Supporting Information). For selected species, the derived Fp, from the data obtained during low SO42− conditions, versus Morg are shown in Figure S6 and Tables S2 and S3 of the Supporting Information). As expected, derived Fi,p depends upon the actual organic mass and the data are generally following eq 2. There are agreements with some previous literature,15,43 but the difficulty of this measurement and dependence upon M org makes the comparison complex.15 Another difference to Ki is that Fi,p is derived for each data point, a specific Morg, while Ki 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 Fi,p also present the derived effective saturation concentration constant (C0eff) for selective compounds that can easily be converted to Ki (C0eff = 1/Ki). Generally, our study provides much higher Ki for the masses corresponding to the compounds in that study. For the five of the selected compounds illustrated in the Supporting Information, we derive Ki 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 performed in the much warmer southeastern United States and central Amazonia, the absolute comparison was fair. In addition, both studies agree on a much lower Ki for pinonic acid compared to the other five compounds. Generally, the FIGAERO method has been scrutinized in various ways.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 toward acids and 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 higher molecular weight 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 upon the air mass origin or the sulfate content of the aerosol. Generally, the comparison of Ki 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 favor of an air mass effect; however, we could not detect any differences in Tmax between the low versus high sulfate subset, and the effect would rather enhance the particle phase signal, providing higher Ki than expected from the linear

6. CONCLUSION In conclusion, the partitioning of the 640 compounds measured as ions with different molecular weights 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 conditions, 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., γi) or the uptake kinetics (mass transfer limitation). The study demonstrates that partitioning of compounds in the complex ambient atmosphere follows ideal Raoult’s law for some conditions and challenges new measurements in more polluted environments.

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ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acsearthspacechem.9b00041. Example on desorption peak evaluation (Figure S1), Ki calculated from the whole campaign and from the two subsets (high and low SO42−) versus corresponding Tmax (Figure S2), box plot illustrating the variability of Ki during the measurement campaign (Figure S3), particle number concentration during the campaign (Figure S4), back-trajectory calculation during a change from high to low SO42− conditions (Figure S5), partitioning using Fi,p including a comparison to the literature (Figure S6 and corresponding description), calculated Ki for the 100 constituents with the highest R2 (Table S1), average Fi,p for selected compounds for day and night conditions, together with some previous measurements (Table S2), and site descriptions for data in Table S2 (Table S3) (PDF)

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AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. ORCID

Joel A. Thornton: 0000-0002-5098-4867 Mattias Hallquist: 0000-0001-5691-1231 Present Addresses §

Michael Le Breton: Volvo Trucks, Herkulesgatan 75, SE-405 08 Gothenburg, Sweden. ⊥ Felipe D. Lopez-Hilfiker: TofWerk AG, Uttigenstrasse 22, 3600 Thun, Switzerland. Notes

The authors declare no competing financial interest. 1285


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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 2014-05332) and Formas (Grants 214-2010-1756 and 942-2015-1537). The authors thank Liqing Hao and Annele Virtanen for providing AMS sulfate data.

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Gas to Particle Partitioning of Organic Acids in the Boreal Atmosphere

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