Measuring and Modeling the Salting-out Effect in Ammonium Sulfate

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Measuring and Modeling the Salting-out Effect in Ammonium Sulfate Solutions Chen Wang, Ying Duan Lei, Satoshi Endo, and Frank Wania Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/es5035602 • Publication Date (Web): 16 Oct 2014 Downloaded from http://pubs.acs.org on October 29, 2014

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Environmental Science & Technology

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Measuring and Modeling the Salting-out Effect in Ammonium Sulfate

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Solutions

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Chen Wang,1 Ying Duan Lei,1 Satoshi Endo,2 Frank Wania1,*

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1

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of Toronto Scarborough, 1265 Military Trail, Toronto, Ontario, Canada M1C 1A4

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2

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Environmental Research, Permoserstrasse 15, D-04318 Leipzig, Germany

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* To whom correspondence should be addressed: [email protected], +1-416-287-7225

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Abstract. The presence of inorganic salts significantly influences the partitioning behavior of

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organic compounds between environmentally relevant aqueous phases, such as sea water or

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aqueous aerosol, and other, non-aqueous phases (gas phase, organic phase, etc.). In this study,

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salting-out coefficients (or Setschenow constants) (KS [M-1]) for 38 diverse neutral compounds in

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ammonium sulfate ((NH4)2SO4) solutions were measured using a shared headspace passive

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dosing method and a negligible depletion solid phase microextraction technique. The measured

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KS were all positive, varied from 0.216 to 0.729, and had standard errors in the range of 0.006-

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0.060. Compared to KS for sodium chloride (NaCl) in the literature, KS values for (NH4)2SO4 are

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always higher for the same compound, suggesting a higher salting-out effect of (NH4)2SO4. A

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polyparameter linear free energy relationship (pp-LFER) for predicting KS in (NH4)2SO4

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solutions was generated using the experimental data for calibration. pp-LFER predicted KS

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agreed well with measured KS reported in the literature. KS for (NH4)2SO4 was also predicted

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using the quantum-chemical COSMOtherm software and the thermodynamic model AIOMFAC.

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While COSMOtherm generally overpredicted the experimental KS, predicted and experimental

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values were correlated. Therefore, a fitting factor needs to be applied when using the current

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version of COSMOtherm to predict KS. AIOMFAC tends to underpredict the measured

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KS((NH4)2SO4), but always overpredicts KS(NaCl). The prediction error is generally larger for

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KS(NaCl) than for KS((NH4)2SO4). AIOMFAC also predicted a dependence of KS on the salt

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concentrations, which is not observed in the experimental data. In order to demonstrate that the

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models developed and calibrated in this study can be applied to estimate Setschenow coefficients

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for atmospherically relevant compounds involved in secondary organic aerosol formation based

Department of Chemistry and Department of Physical and Environmental Sciences, University

Department of Analytical Environmental Chemistry, UFZ - Helmholtz Centre for

ACS Paragon Plus Environment

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on chemical structure alone, we predicted and compared KS for selected α-pinene oxidation

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products.

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Introduction

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Atmospheric aerosol particles, cloud and fog droplets often contain both organic species and

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inorganic salts.1,2 The inorganic salts’ impact on the activity or solubility of neutral organic

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solutes in aqueous solutions can be described by the following empirical equation:

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log(γ/γ0) = log(S0/S) = KS [salt]

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where γ0 and γ are activity coefficients of an organic solute in pure water and salt solutions, S0

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and S are the solubilities of a solute in pure water and salt solutions, KS (M-1) is the empirical

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Setschenow or salting-out coefficient and [salt] (mol/L) is the salt solution concentration. The

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salting-out effect can also be described in an alternative form as follows:3

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log(K1/salt water/K1/water)= KS [salt]

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where K1/salt water and K1/water are equilibrium partitioning coefficients of an organic solute between

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the aqueous phase and another non-aqueous phase (as described in equation 1 in ref.3). When KS

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is positive, there is a shift of the partitioning equilibrium to the non-aqueous phase due to the

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salting-out effect, and vice versa. This effect has important implications regarding the reactivity,

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transport and fate of organic compounds in atmospheric waters, where the concentration of

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dissolved salts often varies widely with decreasing water content of a hydrometeor, i.e. from

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cloud droplets to fog droplets to aerosol particles.2,4 The influence can be particularly significant

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for aerosol particles because of the relatively large amount of salts present. These inorganic salts

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not only contribute to the total mass of aerosol,1 but also play an important role in the phase

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partitioning equilibria of organic species involved in secondary organic aerosol formation.5 In

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addition, the inorganic salts together with the cycle of relative humidity in the atmosphere

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influence the phase transition of aerosol particles.6 Liquid-liquid phase separation was observed

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to occur in both real-world and laboratory-generated particle samples containing ammonium

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sulfate and secondary organic materials, forming an inorganic rich phase and an organic rich

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phase.7 In the electrolytes-organics mixture, ions have a high affinity for water and a low affinity

(1)

(2)


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for organic compounds, especially the non-polar compounds, and two phases will form in order

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to maintain a minimum system free energy.5,6

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Among the various inorganic salts present in atmospheric aerosol, cloud, and fog in continental

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regions, ammonium (NH4+), sulfate (SO42-), and nitrate (NO3-) are often the most abundant.

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Marine aerosols additionally include sodium (Na+) and chloride (Cl-).1 When comparing the

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salting-out effect of SO42-, NO3- and Cl-, several studies8,9 found NO3- to have the smallest and

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SO42- the highest effect.

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Most of the KS values reported so far were for NaCl or seawater, which is relevant for the marine

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environment and to some extent for atmospheric water. However, Setschenow constants for

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organic compounds in (NH4)2SO4 solution (subsequently referred to as KS((NH4)2SO4)) are

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available only for a very limited number of chemicals. When Xie et al.10 reviewed the reported

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data for Setschenow constants in various salt solutions in 1997, KS((NH4)2SO4) was available for

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only six aromatic solutes. Görgényi et al.9 measured salting-out effects of 27 different salts,

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including (NH4)2SO4, but only for four organic solutes. Furthermore, most of the previous

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measurements were performed under relatively low salt concentrations (0.5 M, i.e., similar to the

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salinity of sea water), whereas the salting-out effect in highly concentrated salt solutions is more

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relevant in the atmosphere: concentrations of inorganic salts in aqueous aerosols can be very

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high (i.e. reach saturation or even supersaturated concentrations). In addition, no data are

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available to systematically compare the salting-out effect for the two major salts in the

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environment, NaCl and (NH4)2SO4. As a consequence, more KS((NH4)2SO4) are required for

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accurate atmospheric phase distribution assessments of organic compounds, including those

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implicated in secondary organic aerosol (SOA) formation. Particularly, measurements at higher

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salt concentrations are required to test the dependence of KS on salt concentrations.

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One reason for this lack of empirical data might be that the determination of Setschenow

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coefficients requires highly precise measurements of aqueous solubility or partitioning properties

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(i.e., air-water, organic solvent-water) at different ionic strength. Until recently, few methods

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allowed for a relatively simple, reliable and inexpensive determination of the salting-out effect

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for diverse compounds. Jonker et al.11 proposed a solid phase microextraction (SPME) technique,

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where KS in NaCl solutions (subsequently referred to as KS(NaCl)) is determined by measuring


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SPME fiber-water partitioning coefficients at different salt concentrations. Endo et al.3 modified

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this SPME method, developed another shared headspace passive dosing (SHPD) approach to

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measure KS, and applied both methods to determine KS(NaCl) for a large number of organic

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solutes with diverse structures. Their large and reliable data set also provided a basis for the

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development of a predictive model.3

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Due to the lack of data on the salting-out effect of (NH4)2SO4 and the significant role of

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(NH4)2SO4 in the atmosphere, the objective of this work was to measure Setschenow coefficients

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for a large and diverse group of organic compounds in (NH4)2SO4 solutions at a wide range of

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salt concentrations using recently developed experimental approaches described in the literature.

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While it may seem more sensible to measure the salting-out effect for atmospherically relevant

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substances directly, such compounds are not always commercially available and may often pose

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experimental challenges. In order to be able to compare KS((NH4)2SO4) with KS(NaCl), the

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organic compounds tested here were those used in previous work for NaCl.3 These compounds

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were not meant to represent compounds found in aerosols, but were selected to include

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compounds with diverse functional groups that interact by a variety of intermolecular forces.

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This was necessary, because we aimed to use the measured data to develop and test predictive

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models for estimating KS((NH4)2SO4) from chemical structures. Models calibrated with such a

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diverse dataset can be applied to predict KS for other compounds, including those found in

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aerosol.

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Materials and Methods

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Setschenow coefficients for 38 neutral organic compounds in (NH4)2SO4 solutions were

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measured in this study (names are listed in the first column of Table 1).

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Materials. The 38 organic chemicals were purchased from Sigma Aldrich. Solvents ethyl acetate,

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hexane, methanol and acetone (Omnisolv) were obtained from EMD Millipore Chemicals.

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Ammonium sulfate and sodium chloride in analytical grade were from VWR and Fisher

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Scientific, respectively. Olive oil was purchased from a grocery store in Toronto. Water was

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treated with a Barnstead Nanopure water system. Polydimethylsiloxane fibers (PDMS, 30 µm

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coating thickness, 13.2 µL/m coating volume) and polyacrylate fibers (PA, 36 µm coating

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thickness, 16.5 µL/m coating volume) were from Polymicro Technologies (Phoenix, AZ).


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Experimental Methods. The methods for the determination of KS include the SHPD method and

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negligible depletion SPME technique as detailed in Endo et al.3 The SHPD method and SPME

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method were compared in Endo et al.3 using a series of 2-alkanones. The two methods showed

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good agreement, although the SPME method has slightly lower standard errors. One of the two

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methods was chosen for a compound based on its air-water partitioning behavior. The SHPD

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approach is suitable for relatively volatile compounds (e.g., alkylbenzenes) and for compounds

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that tend to adsorb to interfaces (e.g., highly fluorinated compounds). The fiber types in the

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SPME method were chosen to avoid significant solute depletion and also to reduce the

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equilibration time for the compounds between fiber and the solutions.3

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In brief, 27 compounds were measured with the SPME method, where SPME fibers (PA or

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PDMS, 2 cm length) were deployed in the same volume (8 mL) of pure water or salt solutions

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spiked with the same amount of test solutes (Table S1 in Supporting Information). Due to the

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small volume of the fibers, only negligible amounts of test solutes will be absorbed and the

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aqueous concentrations will not significantly decrease. At equilibrium (more than 24 h

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deployment), the relative concentrations in the fibers placed in pure water and in salt solutions at

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different concentrations are a measure of the degree of salting-out. Four replicates were made for

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each salt concentration. The fibers were extracted in 500 µL ethyl acetate and analyzed with gas

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chromatography mass spectrometry (GC/MS). Measurements were made in solutions with up to

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six different concentrations of (NH4)2SO4, which were 0%, 5%, 10%, 20%, 30% and 40% (w/v),

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corresponding to molar concentrations of 0, 0.38, 0.76, 1.52, 2.27 and 3.03 M.

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The SHPD method was applied in measuring KS for 11 relatively volatile compounds in

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(NH4)2SO4 solutions (Table S1 in Supporting Information). Briefly, in a closed container, test

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solutes transfer from the spiked non-volatile solvent (100 mL olive oil) through the shared

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headspace into pure water and salt solutions (25 mL) in small petri dishes as described in Figure

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1 of ref.3. At equilibrium, 800 µL (×4 replicates) of aqueous solutions were sampled through a

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septum using a gas tight syringe and then liquid-liquid extracted with 600 µL of n-hexane.

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Extracts were analyzed using GC/MS. The differences in concentrations in the different aqueous

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solutions reflect the degree of salting-out. In this method, three or four salt concentrations (0%,

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5%, 10%, and 20 % (w/v)) were used depending on the solubilities of the compounds.


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With pH values of the (NH4)2SO4 solutions in this study varying from 5.28 to 5.5 without

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buffering, all organic solutes were predominantly in the neutral form during the experiment. In

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both methods, KS can be determined by linear regression analysis based on eqs. 2 and 3 in ref.3

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More detail on these two methods is provided elsewhere3 and can be found in the Supporting

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Information of this article (Table S1). To validate the measured data, a few measurements were

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made with a third method, namely a headspace gas chromatographic (HS-GC) method. Details

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for this method are given in the Supporting Information. Because previous studies found KS to be

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independent of the solute concentrations11,12 and only weakly dependent on temperature,13,14 only

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one organic solute concentration and one temperature (room temperature) were used in the

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experiments.

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GC/MS Analysis. The compounds were analyzed on a GC/MS system, consisting of a 6890 GC

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with a 5973 MS detector and a 7683 autosampler (Agilent Technologies). Helium was used as

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carrier gas (1.2 mL/min). The columns used were DB-5 (60 m × 0.25 mm i.d. × 0.25 µm, Agilent

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J&W, USA) and DB-WAX (30 m × 0.25 mm i.d. × 0.25 µm, Agilent J&W, USA). The GC oven

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temperature program was different for different analytes. The MS was operated in electronic

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ionization and selective ion monitoring mode with a GC/MS interface temperature of 250 °C.

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The samples (1 µL) were injected in splitless mode and the injector temperature was held at

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225 °C. Internal standards were used for quantification of analytes. More details on columns and

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internal standards can be found in Supporting Information (Table S1).

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Modeling. Three different modeling approaches were used to predict an organic chemical’s

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KS((NH4)2SO4). While the quantum-chemical COSMOtherm software and the group contribution

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method AIMOFAC15 only require information on the solute’s molecular structure, the poly-

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parameter linear free energy relationship (pp-LFER) approach depends on the availability of

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empirical data for calibration.

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Multiple linear regression analysis and the experimentally determined KS values for 38 solutes

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were used to calibrate a pp-LFER for KS((NH4)2SO4). Specifically, experimental KS values can

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be correlated with compound specific solute descriptors using:3

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KS = c + aA + bB + sS + eE + vV

(2)


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where A, B, S, E and V are solute descriptors representing a solute's hydrogen-bond acidity,

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hydrogen-bond basicity, dipolarity/polarizability, excess molar refraction and McGowan volume

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(cm3/mol) divided by 100, respectively. The regression coefficients are denoted by a, b, s, e and

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v; c is the regression constant. Such a pp-LFER describes, and can be used to predict, the salting-

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out effect based on molecular interaction properties.3 Previously, pp-LFERs have been shown to

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reliably predict KS(NaCl).3 Solute descriptors were taken from the UFZ-LSER database16 or

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estimated with Absolv from ACD/Labs (Advanced Chemistry Development, Inc., Toronto,

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Canada) and are given in the Supporting Information.

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KS((NH4)2SO4)

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BP_TZVPD_FINE_C30_1401 parameterization, COSMOlogic GmbH & Co. KG, Leverkusen,

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Germany 2014), which relies on a quantum mechanical calculation and a statistical

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thermodynamics approach to predict physical-chemical properties.17,18 In this approach, structure

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files (called COSMO files) of organic solutes and salt ions, which had been optimized with

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TURBOMOLE, are fed into a program that calculates partition coefficients between pure water

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and (NH4)2SO4 solutions (Kwater/salt water) at a temperature of 298 K. The term K1/salt water/K1/water in

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the Setschenow relationship (eq. 2) is equivalent to Kwater/salt water, so KS [M-1] can be calculated

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from the salt concentration and log Kwater/salt water.

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AIOMFAC is a thermodynamic model that predicts activity coefficients of different chemical

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species in inorganic-organic mixtures based on a group-contribution approach.15 In the present

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study, AIMOFAC was used to calculate activity coefficients of compounds in pure water and salt

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solutions at 298 K. Setschenow constants can then be calculated from eq. 1. AIOMFAC requires

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the input of temperature, and the mole fractions of organic solute and inorganic salt in the

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mixture. Since a very small amount of the organic solute was used (0.05), it

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was excluded from the final regression model, indicating that the excess molar refraction does

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not influence the salting-out process significantly. The standard errors for all the regression

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coefficients are small, from 0.025 to 0.045. As in the pp-LFER equation for KS(NaCl),3 the

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regression coefficients for A, B and S are negative, while that for V is positive. This indicates that

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the salting-out effect of (NH4)2SO4 is negatively related to the ability of the target compounds to

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engage in polar interactions (hydrogen-bond interaction and dipole interaction), while it is

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positively related to the molecular size of the compounds, which agrees with previous findings

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from other studies.3,10,12 The main difference for the two salts is that the values of a, b and s were

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much closer to 0 for NaCl (from -0.060 to -0.042)3 than those for (NH4)2SO4 (from -0.194 to -

(3)


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0.085). As explained in Endo et al.,3 the presence of NaCl increases the polar interaction energies

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of aqueous solutions toward neutral organic solutes. For (NH4)2SO4, this increase of the polar

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interactions is much larger as indicated by the larger influences of a, b and s. This means the

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systematically higher KS((NH4)2SO4) compared to KS(NaCl) is mostly due to the coefficient v for

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molar volume (0.287 vs. 0.171) and the constant term c (0.300 vs. 0.112).

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Measured Setschenow constants in (NH4)2SO4 solutions have been rarely reported and only 10

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data, mostly for aromatic compounds, were found in the literature. Comparisons were made

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between these experimental data from the literature9,10 and estimations with the four-descriptor

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pp-LFER model (Figure 2). Setschenow constants obtained using the pp-LFERs model are in

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excellent agreement with experimental data, despite the large uncertainties and the different

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methods used for measuring KS in the literature. This further supports that the pp-LFER model

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correctly accounts for the intermolecular interactions in the ion-organic solute-water system. 1.0 data from this study data from literature

measured KS((NH4)2SO4)

0.8

1:1 line 0.6

0.4

0.2

0.0 0.0

0.2

0.4

0.6

0.8

1.0

pp-LFER estimated KS((NH4)2SO4)

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Figure 2. Measured and pp-LFER calculated KS values for (NH4)2SO4. Squares and circles represent data from this study and the literature, respectively. The line indicates 1:1 agreement.

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COSMOtherm Calculation. Partition coefficients between pure water and (NH4)2SO4 solution

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(Kwater/salt

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compounds in this study and 10 compounds from the literature.9,10 Values of log Kwater/salt water for

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all compounds are positive, indicating a salting-out effect of (NH4)2SO4. COSMOtherm

water)

were calculated using COSMOtherm at a temperature of 298 K for the 38


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predictions were made at different (NH4)2SO4 concentrations, 5%, 10%, 20%, 30%, and 40%

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(w/v), which cover the experimental concentration range. Setschenow constants calculated at

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different salt concentrations are almost identical (SD from 0.01 to 0.05 for all the 38 compounds),

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so only KS calculated at 5% (w/v) are reported in the following discussion.

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COSMOtherm predictions are higher than the experimental KS((NH4)2SO4) values for all the

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compounds. The ratios between COSMOtherm-predicted and experimental KS values range from

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2.1 to 4.5, with an average of 2.7. Other studies using COSMOtherm to predict KS also noted

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overestimation problems.3,22,23,24 Although KS((NH4)2SO4) values were higher than the

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experimental ones, there was a correlation between COSMOtherm predictions and measurements

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(R2=0.58, SD=0.078) (Figure 3, green line). Tri-n-butyl phosphate and bisphenol A are two

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outliers in this linear regression. While the discrepancy for tri-n-butyl phosphate is likely mainly

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due to the measurement errors discussed before, no obvious reasons could be identified for

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bisphenol A. Uncertainties in both measurement and COSMOtherm calculations could have

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contributed to the relatively poor correlation. By removing these two compounds, the R2 of the

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correlation increased to 0.74 (SD=0.062) (Figure 3, purple dashed line). A relationship between

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predicted and measured KS (KS = 0.334 KS,COSMOtherm + 0.0604 ) is suggested to be used in the

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future for correction of the salting-out effect of (NH4)2SO4 predicted by COSMOtherm. This

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relationship between COSMOtherm prediction and measured KS((NH4)2SO4) values is almost

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identical to a similar relationship derived for KS(NaCl).3

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AIOMFAC Calculation. Activity coefficients at 298 K of different organic compounds in water

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at six different concentrations of either (NH4)2SO4 (0, 5, 10, 20, 30, 40 % (w/v)) or NaCl (0, 6,

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10, 14, 20, 36 % (w/v)) were predicted using AIOMFAC and KS values were then calculated

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using eq. 1. As a group contribution method, AIOMFAC critically depends on the availability of

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the appropriate group interaction parameters. Because AIOMFAC was developed primarily for

325

atmospheric applications, such parameters are available for only 17 of the compounds with

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experimental KS for (NH4)2SO4 and NaCl reported here and by Endo et al.3 (Table S2). Moreover,

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as the error bars in Figure 4 show, the AIOMFAC-predicted KS varied substantially at different

328

salt concentrations. For both (NH4)2SO4 and NaCl, variations are large for compounds with a

329

higher KS. The AIOMFAC-estimated KS values for (NH4)2SO4 vary more with the salt

330

concentration than those for NaCl. A large variation of KS at different salt concentrations

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(especially for (NH4)2SO4) contradicts our experimental observations.


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1.0 data from this study data from literature

measured KS((NH4)2SO4)

0.8 y = 0.334x + 0.0604 R² = 0.74

0.6

tri-n-butyl phosphate 0.4 bisphenol A 0.2

y = 0.261x + 0.144 R² = 0.58 1:3 line

0.0 0.0

0.5

1.0

1.5

2.0

2.5

3.0

COSMOtherm prediced KS((NH4)2SO4)

332 333 334 335 336

Figure 3. Measured and COSMOtherm calculated KS values for (NH4)2SO4. The black line indicates the 1:3 relation between measured and COSMOtherm estimated values. The green line and equation indicate the linear regression for all plotted data. The purple dashed line and equation indicate the regression without tri-n-butyl phosphate and bisphenol A.

337

The AIOMFAC predicted-KS, averaged over the six salt concentrations, is compared with the

338

measured KS for (NH4)2SO4 and NaCl in Figure 4. AIOMFAC tends to underpredict the

339

measured KS((NH4)2SO4), but always overpredicts KS(NaCl). The prediction error is generally

340

larger for KS(NaCl) than for KS((NH4)2SO4).

341

To illustrate the difference between AIOMFAC and pp-LFER predictions more clearly, the

342

AIOMFAC-predicted KS is compared directly with the pp-LFER-predicted KS for 102

343

compounds, including the 17 compounds for which experimental KS are available here and in

344

Endo et al.3 (Figure 4). The compounds used and their solute descriptors are given in Table S3 in

345

the Supporting Information. The compounds used for this comparison all fall within the pp-

346

LFER model’s applicability domain according to a leverage calculation. AIOMFAC and pp-

347

LFER-predicted KS are correlated for both (NH4)2SO4 (R2=0.533, SD=0.156) and NaCl

348

(R2=0.803, SD=0.102). However, the relationship differs from 1:1 agreement and is different

349

between the two salts: AIOMFAC-predicted KS(NaCl) are mostly higher than pp-LFER

350

predictions, whereas AIOMFAC-predicted KS((NH4)2SO4) are more scattered.


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1.4

(NH4)2SO4 y = 1.206x - 0.217 R² = 0.53

1.2 1.0

AIOMFAC predicted KS(NaCl)

AIOMFAC predicted KS((NH4)2SO4)

1.4

1:1 line

0.8 0.6 0.4 Ks predicted by pp-LFER

0.2

y = 2.797x - 0.209 R² = 0.80

1.2 1.0

1:1 line

0.8 0.6 0.4 Ks predicted by pp-LFER

0.2

Ks measured in this study

0.0

Ks measured by Endo et al.

0.0 0.0

351

NaCl

0.2

0.4

0.6

0.8

1.0

1.2

measured or pp-LFER predicted KS((NH4)2SO4)

1.4

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

measured or pp-LFER predicted KS(NaCl)

352 353 354 355 356 357

Figure 4. AIOMFAC and measured or pp-LFER calculated KS values for (NH4)2SO4 (left) and NaCl (right). The AIOMFAC predictions are the average of the KS calculated at six different salt concentrations and the error bar shows the standard deviation. In both figures, the orange triangles show compounds with experimental KS values in this study and in Endo et al.3 The solid line indicates 1:1 agreement and the dashed line designates the linear regression between two prediction methods.

358

Relative Merits of the Prediction Methods. Each of the three different approaches to predict

359

the salting-out effect has merits and limitations. Both pp-LFER and AIOMFAC methods are easy

360

to apply because of their simplicity and general availability of model parameters. However, they

361

also have limitations. As with any other empirical and semi-empirical prediction, these models

362

are limited by their calibration data. For instance, even though the 38 calibration compounds in

363

this study were selected to represent compounds with solute descriptors covering a wide range,

364

the pp-LFER approach might still not work well for compounds that are outside of its

365

applicability domain. A statistical analysis such as the leverage calculation is recommended

366

before applying the pp-LFER to a compound to test whether the compound falls within the

367

model’s applicability domain. The group contribution method AIOMFAC, as discussed

368

previously, is limited by the availability of interaction parameters for the functional groups

369

present in a compound and will overlook intramolecular interactions that are not captured during

370

the calibration. COSMOtherm, on the other hand, is based on quantum mechanical calculations

371

and requires no experimental data. In theory, it should be able to calculate KS for any compound

372

given its molecular structure. It is also the only one of the three methods that considers different


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conformations of a molecule, which should give more realistic results. However, based on the

374

results of the present and previous studies, the current version of COSMOtherm always tends to

375

overpredict the salting-out effect3,22,23,24 and predictions for some compounds can be outliers,

376

such as bisphenol A for (NH4)2SO4 or caffeine for NaCl.3 While more investigations are needed

377

to improve this approach, based on this work on (NH4)2SO4 and early work on NaCl,3

378

COSMOtherm in combination with a calibrated regression is still a promising tool for predicting

379

the salting-out effect of diverse compounds. Because laboratory measurements can hardly be

380

performed under supersaturated conditions, prediction models are of great significance for such

381

situations. Among all the three models, only AIOMFAC calculates activity coefficients of

382

electrolytes, water, and organics at highly concentrated and even supersaturated conditions. The

383

experimental data for the pp-LFER and the regression with COSMOtherm predictions were

384

obtained in (NH4)2SO4 solution up to 3 M. The applicability of these two methods to predict the

385

salting-out effect in supersaturated solutions needs further testing.

386

Implications for Chemical Partitioning of SOA Compounds. COSMOtherm, pp-LFER

387

and AIOMFAC were applied to study the influence of the salting-out effect on the partitioning of

388

fourteen α-pinene oxidation products between gas and aqueous phase. They were chosen because

389

of their known contribution to secondary organic aerosol formation and the availability of

390

structural information in the literature.5 Structures and Absolv-predicted solute descriptors for

391

these compounds are given in Table S4 in the Supporting Information. Setschenow constants for

392

(NH4)2SO4 predicted with these three approaches are shown in Figure 5. The linear regression in

393

Figure 3 was used to correct the COSMOtherm-predicted KS. Predictions from pp-LFER and

394

COSMOtherm are very close, while AIOMFAC predicts a higher salting-out effect for the

395

fourteen compounds. COSMOtherm-predicted Henry’s law constants or air-water partitioning

396

coefficients (Kair/water, L water/L air) of these compounds range from 2.2×10-16 to 4.2×10-7 (L

397

water/L air) at 25 °C, covering 9 orders of magnitude. According to the KS predicted with pp-

398

LFER and COSMOtherm, Kair/water for the fourteen α-pinene oxidation products increases by

399

approximately a factor of 10 to 100 due to the presence of 4 M (NH4)2SO4 (saturation

400

concentration at 25°C). Concentrations of (NH4)2SO4 in aqueous aerosols often exceed saturation

401

and also other types of salts may be present, which means there will be even more salting-out if

402

additivity of the salt effects in mixed electrolytes is assumed.25 The increase in Kair/water when

403

using the AIOMFAC-predicted KS is higher, ranging from 100 to 10,000 times for the fourteen


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404

compounds. As previously discussed, AIOMFAC-predicted KS could possibly have a larger error

405

compared to the other two methods.

406

The ions present in aerosol water are predominantly NH4+, Na+, H+, SO42-, NO3-, and Cl-. Very

407

few research has focused on the salt effect of individual ions.9 Even though additivity is always

408

assumed when salt effects for electrolytes mixtures are considered (e.g. AIOMFAC), this

409

assumption should be tested experimentally and methods should be developed to separately

410

quantify the salt effect of individual ions. This would help in the development of models for

411

predicting the salting-out effect of any combinations of ions for any electrolyte mixtures, such as

412

complex aerosol water systems. 1.4 1.2

KS ((NH4)2SO4)

1.0

pp-LFER COSMOtherm, average AIOMFAC, average

0.8 0.6 0.4 0.2 0.0

413 414 415 416 417 418

Figure 5. KS((NH4)2SO4) for 14 α-pinene oxidation products predicted with COSMOtherm, ppLFER and AIOMFAC. Structures of the 14 compounds5 are given in Table S4 in the Supporting Information. COSMOtherm and AIOMFAC predictions are the average of KS at six different salt concentrations and the error bar shows the standard deviation. Values for COSMOtherm are corrected with the regression equation in Figure 3.

419

Finally, we should note that the methods introduced here only predict the extent to which

420

the presence of ammonium sulfate affects the solvation of organic compounds in water.

421

They cannot quantify how the presence of salts might influence the reactive fate of the

422

solvated organic compounds in water. An example is the effect of ammonium sulfate on the

423

hydration reaction of glyoxal or the reaction of the salt with glyoxal.26-28

424

Acknowledgement


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We acknowledge funding from the Natural Sciences and Engineering Research Council of

426

Canada.

427

Supporting information Available

428

A table with information on the methods used for each compound, tables with solute descriptors

429

and a description of the headspace gas chromatographic method. This material is available free

430

of charge via the Internet at http://pubs.acs.org.

431

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Measuring and Modeling the Salting-out Effect in Ammonium Sulfate

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