Sorption of Diverse Organic Vapors to Snow - Environmental Science

Swiss Federal Institute for Environmental Science and Technology (EAWAG), ... This not only includes the sorption to the air/snow interface itself but...
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Environ. Sci. Technol. 2004, 38, 4078-4084

Sorption of Diverse Organic Vapors to Snow CHRISTINE M. ROTH,* KAI-UWE GOSS,* AND R E N EÄ P . S C H W A R Z E N B A C H Swiss Federal Institute for Environmental Science and Technology (EAWAG), Ueberlandstrasse 133, P.O. Box 611, CH-8600 Duebendorf, Switzerland, and Swiss Federal Institute of Technology (ETH), Zurich, Switzerland

Sorption from air to one snow sample has been measured for a broad set of organic vapors covering a wide range of physicochemical properties. Those results that could be compared to literature values mostly lay in the same order of magnitude. As expected, a fit with the vapor pressure did not reveal a good correlation (R 2 ) 0.11). Therefore, the data set was interpreted with a linear free energy relationship, based on intermolecular interactions (van der Waals interactions and hydrogen bond interactions). Although we cannot assign the observed sorption to a specific process (adsorption to the snow crystal surface, incorporation in the solid ice crystal, absorption into a quasi-liquid layer, or grain boundary effects), the model provides a useful tool for the prediction of snow sorption for other compounds: log Ki snow surface/air ) 0.639 ((0.037) log Ki hexadecane/air + 3.38 ((0.17) ∑βi + 3.53 ((0.25) ∑Ri - 6.85. The sorption coefficients measured could be described well with the compound parameters used (subscript i), with an R 2 ) 0.90.

Introduction In the atmosphere as well as on the surface of the earth, in Polar Regions, and at high altitudes as well as at low altitudes in winter seasons, snow and ice represent an important compartment for storage and reactions of environmentally relevant chemicals (e.g., refs 1 and 2). Due to its large surface area, it provides an important capacity for physical exchange (sorption) or reactions catalyzed by the snow/ice surface (1). Since organic pollutants were first detected in snow in remote areas (2, 3), research was conducted in order to understand the processes linked to occurrence of organic pollutants in snow and ice. Snow can scavenge particles and pollutants from the atmosphere during snowfall (e.g., refs 4 and 5). During aging, the snowpack can release scavenged compounds or take up compounds present in the lower layers of the atmosphere. Furthermore, snowpack interstitial air is in exchange with the atmosphere (6, 7). Diverse measurements have been conducted during the last 40 yr to reveal and understand the role of snow, snowpacks, and ice shields on the fate of pollutants in the environment. These studies include measurements of organic compounds in the meltwater of freshly fallen snow or of a snowpack, in the snowpack interstitial air, in the air above a snowpack, and in particles scavenged by snow and which were collected from meltwater (and simultaneously in atmospheric particles) (e.g., refs 5 and 8-11). For a compilation of measurements of organic compounds in snow, see ref 2 or ref 3. * Corresponding authors fax: +41-1-823-52-10; e-mail: goss@ eawag.ch, [email protected]. 4078

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A variety of processes have been postulated to occur to organic compounds in/on snow and ice. These are incorporation in the solid ice crystal, dissolution in the so-called “quasi-liquid layer” (QLL), adsorption on the air/“liquid” interface. In addition, adsorption on incorporated mineral particles, absorption in organic and biotic aerosol particles, and partitioning in interstitial air may occur. Co-condensation and chemical reactions catalyzed by the ice surface (possibly under influence of light and other photochemical reactions in the snowpack) can take place (1, 2, 9, 11-14). To date, only a few attempts have been made to include the compartments snow or ice into environmental fate models (2-5, 14-16). One of the major uncertainties in these models concerns the sorption from the air (interstitial or atmospheric) to the snow or ice surface. This surface is still poorly understood, although a variety of methods have been used to examine the properties of the air/snow or air/ice interface (e.g., ref 17, p 227 ff). The importance of this interface in the environment has been demonstrated (4, 11, 14, 15), and the need for a better understanding of the interfacial sorption has been pointed out (e.g., refs 1, 3, 9, and 16). This not only includes the sorption to the air/snow interface itself but also measurements of the snow specific surface area (SSAsnow), which will determine the capacity of the interface for sorption and any other interfacial process. Therefore, considerable efforts have been made in correct measurements of the SSAsnow (18-23). Currently, at temperatures between -30 and 0 °C the snow or ice surface has been assumed to behave similar to a subcooled water surface, at least with respect to the sorption of nonpolar compounds. This hypothesis is based primarily on research using artificial ice or snow systems. Measurements have been conducted on an ice-coated support (2, 24-28), on artificial “snow” made from chunks of frozen water (2), and on pounded pure and salt-containing ice (29, 30). The similarity of an ice surface to a subcooled water surface is further implied by the presence of a QLL, although there is no agreement on how thick or how liquid this layer is supposed to be (for an overview, see refs 17 and 31). Therefore, sorption to the air/ice interface has been included in fate models by extrapolating a correlation between air/water surface adsorption data and other compound-specific properties like solubility and air/bulk water absorption coefficient (Ki H), octanol-water absorption coefficient (Ki ow) and Ki H, vapor pressure, or vapor pressure in combination with hydrogen-bond properties (2, 30, 32, 33). However, whether this extrapolation is appropriate and representative for a real ice or snow surface remains uncertain, as there is a major lack of experimental sorption data for a variety of organic compounds. The existing database is not consistent and not diverse enough to be extrapolated to other types of compounds, particularly more polar ones. In earlier work, we determined about 1500 adsorption coefficients at 15 °C on various surfaces including water, minerals, and salts (34-36). Because adsorption coefficients of the broad range of compounds of interest cannot be correlated simply to the vapor pressure (log p°L) or the octanol-air partition coefficient (log Ki oa), another more sophisticated model was required (37). As we have demonstrated, adsorption of vapors can be described well with a poly-parameter linear free energy relationship (LFER), based on the van der Waals and the electron donor/acceptor interactions (H-bonds), for a large variety of surfaces of environmental relevance (32, 34-36):

log Ki surface/air ) a log Ki hexadecane/air + b 10.1021/es0350684 CCC: $27.50

∑β + c∑R + const i

i

(1)

 2004 American Chemical Society Published on Web 06/29/2004

TABLE 1. Logarithmic Sorption Coefficients Log Ki snow surface/air (m3/m2) on the Snow Surface for 60 Compounds at -6.8 °C Derived from Measurements,a Corresponding Compound Parameters,b and Comparison of Experimental Ki snow surface/aira to Ki water surface/airc compound

log Ki snow surface/air (m3/m2)

∑ri

∑βi

log Ki hexadecane/air (m3/m2)

Ki snow surface/air (m)/ Ki water surface/air -6.8 °C (m)

∆Hi water surface/air (kJ/mol)

n-octane n-nonane n-undecane non-1-ene oct-1-yne cyclooctane 1,1,2,2-tetrachloroethane 1-chlorohexane 1-chloroheptane tetrachloroethene 1-bromopentane ethylbenzene n-propylbenzene p-xylene styrene 1,2,4-trimethylbenzene indane naphthalene 1-methylnaphthalene chlorobenzene 1,2-dichlorobenzene 1,3-dichlorobenzene 1,4-dichlorobenzene 1,2,4-trichlorobenzene bromobenzene iodobenzene diethyl ether di-n-propyl ether diisopropyl ether di-n-butyl ether MTBE tert-amyl methyl ether TAME tert-butyl ethyl ether ETBE methyl phenyl ether/anisole tetrahydrofurane 1,4-dioxane methyl acetate ethyl acetate n-butyl acetate isobutyl acetate tert-butyl formate methyl benzoate propanone/acetone 2-butanone 3-methylbutan-2-one 4-methylpentan-2-one cyclopentanone cyclohexanone ethanol propan-1-ol propan-2-ol 2-methylpropan-2-ol TBA 2,2,2-trifluoroethanol pentanal benzaldehyde benzonitrile nitrobenzene 2-nitrotoluene aniline thiophenol

-4.41 ( 0.25 -4.21 ( 0.25 -3.32 ( 0.25 -3.97 ( 0.25 -4.18 ( 0.25 -4.06 ( 0.25 -3.78 ( 0.25 -4.11 ( 0.25 -3.65 ( 0.25 -4.41 ( 0.25 -4.27 ( 0.25 -4.10 ( 0.25 -3.64 ( 0.25 -4.00 ( 0.25 -3.90 ( 0.25 -3.70 ( 0.25 -3.72 ( 0.25 -2.98 ( 0.25 -2.25 ( 0.25 -4.20 ( 0.25 -3.63 ( 0.25 -3.74 ( 0.25 -3.81 ( 0.25 -3.53 ( 0.25 -3.80 ( 0.25 -3.69 ( 0.25 -4.33 ( 0.25 -3.74 ( 0.25 -3.77 ( 0.25 -2.98 ( 0.25 -3.80 ( 0.25 -3.53 ( 0.25 -3.63 ( 0.25 -3.60 ( 0.25 -3.84 ( 0.25 -3.00 ( 0.25 -4.14 ( 0.25 -3.69 ( 0.25 -2.87 ( 0.25 -3.00 ( 0.25 -4.07 ( 0.25 -2.44 ( 0.25 -3.73 ( 0.25 -3.55 ( 0.25 -3.38 ( 0.25 -2.98 ( 0.25 -2.81 ( 0.25 -2.38 ( 0.25 -3.04 ( 0.25 -2.60 ( 0.25 -2.64 ( 0.25 -2.40 ( 0.25 -3.01 ( 0.25 -3.71 ( 0.25 -2.95 ( 0.25 -2.85 ( 0.25 -2.89 ( 0.25 -2.59 ( 0.25 -2.14 ( 0.25 -3.65 ( 0.25

0 0 0 0 0.13 0 0.16 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 nv nv 0 0 0 0 0 0 0 nv 0 0.04 0 0 0 0 0 0.37 0.37 0.33 0.31 0.57 0 0 0 0 0 0.26 0.09

0 0 0 0.07 0.1 0 0.12 0.1 0.1 0 0.12 0.15 0.15 0.16 0.16 0.19 0.17 0.2 0.2 0.07 0.04 0.02 0.02 0 0.09 0.12 0.45 0.45 0.45 0.45 0.45 nv nv 0.29 0.48 0.64 0.45 0.45 0.45 0.47 nv 0.48 0.51 0.51 0.51 0.51 0.52 0.56 0.48 0.48 0.56 0.6 0.25 0.45 0.39 0.33 0.28 0.28 0.41 0.16

3.68 4.18 5.19 4.07 3.52 4.33 3.80 3.78 4.28 3.58 3.61 3.78 4.23 3.84 3.86 4.44 4.59 5.16 5.79 3.66 4.52 4.41 4.44 5.25 4.04 4.50 2.02 2.95 2.48 3.92 2.38 nv nv 3.89 2.64 2.89 1.91 2.31 3.35 3.16 nv 4.70 1.70 2.29 2.69 3.09 3.22 3.79 1.49 2.03 1.76 1.96 1.22 2.85 4.01 4.04 4.56 4.88 3.93 4.11

16.3 8.4 7.4 9.3 2.9 11.9 3.1 8.0 9.5 18.8 4.9 5.4 5.2 5.4 6.6 2.9 4.1 5.6 3.9 10.8 7.9 10.1 8.5 3.8 14.3 0.8 1.1 1.0 1.0 0.6 1.0 nv nv 1.7 0.9 0.3 0.8 0.6 2.0 0.7 nv 0.3 1.0 0.8 0.7 1.1 0.3 0.6 0.6 0.7 0.6 0.5 1.0 0.7 0.7 3.5 0.6 1.0 0.5 4.8

-35 -40 -49 -38 -41 -40 -46 -37 -41 -34 -38 -39 -45 -40 -40 -47 -45 -52 -62 -34 -43 -40 -40 -50 -38 -54 -44 -52 -52 -64 -51 nv nv -51 -51 -67 -48 -55 -59 -62 nv -73 -52 -55 -58 -61 -68 -71 -62 -67 -67 -72 -60 -54 -63 -56 -65 -65 -74 -45

minimum value maximum value

-4.41 -2.14

0 0.57

0 0.64

1.22 5.25

0.3 18.8

-74 -34

a

This work, arranged by compound class. b From refs 38-41. c Extrapolated to -6.8 °C with calculated ∆Hi water surface/air (for literature, see text).

where log Ki hexadecane/air is the hexadecane/air partition coefficient at 25 °C (see ref 36 for the temperature of the parameter) (38, 39), which is taken as a measure for the van der Waals interactions; ∑Ri is the electron acceptor (H donor); and ∑βi is the electron donor (H acceptor) of the compound

i, respectively (40, 41). The regression coefficients a-c are related to the complementary surface properties, and “const” depends on the standard state of adsorption. To contribute to a broader database and to the development of a powerful tool for predicting the equilibrium VOL. 38, NO. 15, 2004 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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sorption coefficient at the air/snow interface, we present air/snow surface sorption coefficients (Ki snow surface/air) for 60 compounds covering a wide range of polarity (Table 1). The data were analyzed with eq 1, which yields a model to describe sorption to snow and can be used as a prediction tool.

Theory and Methods Sorption Process on/in Snow and Ice. There is an ongoing debate about the physical and chemical properties of the ice surface (e.g., refs 11, 17, 24, 31, and 42-52). Usually, sorption in/on ice is regarded as adsorption to the ice surface. There is, however, evidence that experimental sorption data cannot always be described solely by physical adsorption. Some authors therefore interpreted the observed sorption by dissolution in the bulk ice or in a surface layer, also called liquidlike layer or QLL as well as adsorption on the QLL/air interface (e.g., refs 24, 43, and 49). Other authors assumed that grain boundaries, veins and nodes (formed by those grain boundaries), or defects in the ice lattice govern the sorption of compounds in or on ice (e.g., 46, 50, and 51). There is also some indication that a combination of processes might contribute to the overall sorption on ice (e.g., ref 11). So far, it remains unclear which processes contribute to what extent to sorption at the ice/air interface. Nevertheless, we believe that snow sorption data are best normalized to the surface area of snow samples. This is justified by the fact that any surface structures or lattice defects should be proportional to the surface area. A QLL is so thin (0.1-50 nm; 17) that its volume should also be proportional to the underlying surface area. However, despite the surface area normalization of our experimental data, in the following we avoid speaking of an adsorption coefficient but rather speak of a sorption coefficient. Snow Sampling. The snow samples were taken in the Central Swiss Alps, on a mountain close to the village of Andermatt (46°37′02′′ N, 8°35′38′′ E), at a height of approximately 2278 m above sea level, on January 10, 2003. A profile was cut about 30 cm deep, 20 cm broad, and 150 cm long into the upper snowpack to gain access to the youngest undisturbed layer. The upper 2 cm of this snow layer was not sampled, as it consisted of snow hardened by the influence of wind (see Results and Discussions). Samples were taken within 1.5 m distance, at a depth of about 10 cm from the same snow layer. This snow layer was firm but finely granulated snow that only fell apart in grains after gentle scraping with a spatula. In a depth of about 17-20 cm, the freshest layer lay on the next hard layer, obviously stemming from an earlier snowfall. The history and the structure of the snowpack indicate that the sampled snow was only for a short time at the surface and therefore not contaminated with dry deposited particles. Air temperature was -12.1 °C above the snow and -12.3 to -13.1 °C within the snow profile. Snow cores were cut out of this snow layer horizontally with a stainless steel column (2.2 cm i.d., 10 cm length) on which a cylindrical “snow cutter” with a sharp edge was screwed. The columns and the snow cutter were precooled for 15 min in a portable freezer. The freezer had been stored in a -20 °C room overnight until leaving the laboratory on the morning of the sampling day. After sampling, the columns were closed immediately by pistons (including frits), which were precooled in a plastic bag buried in the snow, and column endings precooled in the freezer. They were cooled in the portable freezer until return to the laboratory. Two of the five sampled cores could be used for the experiments, as three showed irregular packing or partly plugged frits. From the same snow layer but within a radius of 4 m, three 1-L glasses with plastic screw cap were filled with additional snow. The empty glasses were pushed into the snow layer horizontally, shaken softly to let the snow fall to the bottom of the glass, and pushed into the snow layer again 4080

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until glasses were filled. They were closed and stored in the portable freezer until return to the laboratory. These samples were used for measurements of pH, conductivity, and major ions composition. Sampled cores and glasses were stored in the -20 °C room until use. Sample core 1 was stored for 3 d, sample core 2 was stored for 5 d, and snow-filled glasses were stored for 20 d. Experiments. Sorption coefficients of various organic compounds were determined from their retention volumes by inverse gas chromatography (IGC) (53, 54). Retention times of 60 nonionic, organic compounds (see Table 1; used as received, purity at least 96%, purchased from Fluka and Merck) were measured. From the headspace of the pure compounds, 1-3000 µL of compound-saturated air was taken with a gastight syringe for injection. The experimental system described previously was used (36), with certain adjustments for this specific work. These included a saturated NaCl solution as cooling bath at a temperature of -6.8 ( 0.1 °C for the snow-packed steel columns. In addition, a snow-filled plastic tube (∼30 cm filled, 1.1 cm i.d., glass wool at the outlet) was installed in a bath of coolant (propylene glycol) for presaturation of the carrier gas (N2) with water vapor at -7 ( 0.1 °C. The temperature was chosen somewhat lower than the column bath to humidify the carrier gas as close as possible to 100% RH but to avoid condensation and freezing of water vapor (RH ) 99.7%). The snow-filled columns were allowed to equilibrate with the cooling bath for about 1.5 h before the carrier gas was turned on. Gas flow rates between 6 and 50 mL/min (linear velocity: 1.74-14.5 cm/min) were used. For these flows, pressure drop within the packed columns was minimal (18-180 Pa with total pressures at the column inlet between 450 and 4700 Pa above ambient pressure). The net retention volume (Vi net) was experimentally determined using the following relation:

Vi net ) ti rQcor - Vtracer

(2)

where ti r is the retention time of the compound, Qcor is the volumetric gas flow rate corrected for the pressure drop across the column, and Vtracer is the elution volume of an inert tracer (methane). The retention time was determined by the first statistical moment of the peak (ref 53, p 69 ff). The retention volume is related to the sorption coefficient (Ki snow surface/air) and the total snow surface area available (Atot) by

Ki snow surface/air ) Vi net/Atot

(3)

where Atot ) msnowSSAsnow, which is simply the product of the snow mass (msnow) and the snow specific surface area (SSAsnow). Note that eq 3 assumes linear sorption isotherm and sorption equilibrium.

Results and Discussions Meteorological History of the Sampled Snow Layer. Meteorological data for the time before sampling was obtained from various stations of the Swiss Federal Institute for Snow and Avalanche Research (SLF, Davos). Interpretation of these data (see Supporting Information, Figures A-D) shows that the sampled snow layer had fallen 5-6 d prior to sampling. Air temperature during this aging period was constantly below 0 °C in the region. Temporary melting of the snow grains can therefore be ruled out. Specific Surface Area of Snow Samples. The specific surface area (SSAsnow) of the snow samples was not measured because an apparatus for these delicate measurements was not available. Instead, we estimated the SSAsnow using the equations in ref 23, where the SSAsnow of 176 snow samples was measured. They distinguished three classes of snow and divided these in 14 subclasses (types). The types differed in the age of the snow layer and the crystal shapes. The age of

our snow samples excludes the subclasses F1-F4. The subclasses A3 (depth hoar) and A4 (melt-freeze layer or sun crust) as well as S1 (surface hoar) and W1 (airborne or recently wind-blown) obviously do not represent our samples either. Subclass R4 was not used as it consists only of two samples. With the remaining types of snow, eqs 9-14 in ref 23 can be applied to estimate the SSAsnow from the snow densities of the two samples used. The densities were calculated from the water weight after melting the samples, divided by the volume of the column (0.16 and 0.19 g/cm3). Based on these remaining six equations, the average SSAsnow and their errors are similar for both columns: 0.037 ( 0.010 and 0.035 ( 0.008 m2/g. Taking only the equations with R 2 g 0.4 and based on more than two data points (i.e., R1, A2, and A1), the average SSAsnow for our samples is estimated to be 0.035 ( 0.013 and 0.032 ( 0.011 m2/g, respectively. Cabanes et al. reported the evolution of the SSAsnow with time at three different sites (site P, P′, and L) in the French Alps (19) and at a site in the Arctic (18). From their data in the Alps, our estimated SSAsnow are 0.025-0.040 m2/g for a snow layer aged 5-6 d. At site P, the initial snow density (0.21 g/cm3) and the air temperature (-15 to 0 °C, average -11 °C) were most similar to our samples. From the sample evolution at P, we can deduce an SSAsnow of 0.035 m2/g at an age of 5-6 d. Another work on this topic (22) apparently measured values too high because of artifacts (19, 23). Combining all information from refs 23 and 19 with the meteorological history and the packing density of our sample, we used a specific surface area of 0.035 ( 0.010 m2/g for the normalization of our data. If the snow of the sampled layer fell on January 7, the age would be 3 d. Though unlikely, this age could also be derived from Figure C in Supporting Information. This would imply an SSAsnow of 0.045 m2/g determined from the reported SSAsnow evolutions for the same samples P, P′, and L in ref 19. This value lies within the assumed uncertainty. Evaluation of Experimental System. As demonstrated in previous works (34, 36), none of the compounds were retarded in the IGC system with an empty column. The net retention volumes in repeated experiments within one sample core were reproducible with a coefficient of variation (COV) of 10%. Retention of selected compounds showed no systematic shift in retention times at the start and the end of the experiments, indicating constant snow properties throughout the course of the experiments. Repeated experiments with different concentrations of selected compounds (n-nonane, acetone, ethanol, and methyl acetate) over a factor of 2-10 did not show any effect on the retention volume, confirming that concentrations were in the linear range of the isotherm. The carrier gas velocity for ethanol and n-nonane was varied by a factor of 4-8 in the experiment with the second column, indicating no significant nonequilibrium effect. For n-nonane (factor 8), some changes were measured but were discarded as outliers because ethanol (factor 4) with a 10-fold larger retention volume was in equilibrium. Differences between the two separately “packed” columns were up to a factor 2 and have been taken into account in the calculation of the error by assuming a relative uncertainty of Vi net of 50%. We assign the differences to slightly irregular packing of the second column, as asymmetries of the peaks were somewhat higher. Sorption Coefficients. The Ki snow surface/air values and the corresponding errors for 60 compounds covering a wide range of physicochemical properties and 2.5 orders of magnitude in log Ki snow surface/air are listed in Table 1. The errors have been calculated by the following formula:

∆ log Ki snow surface/air )

x( ) ([ ] [ 1 ln 10

2

∆Vi net Vi net

2

+

] [

∆msnow msnow

2

+

])

∆SSAsnow SSAsnow

2

(4)

where Vi net is the net retention volume, msnow is the snow mass, and SSAsnow is the snow specific surface area. The uncertainty of ∆SSAsnow is 0.01 m2/g, of ∆msnow is 0.2 mg, and ∆Vi net is 50% of Vi net. This latter uncertainty is large because of problems with the reproducibility of packing. The relative uncertainty of Ki snow surface/air is 58% for all compounds and is dominated by the uncertainty in Vi net (50%), followed by the uncertainty in SSAsnow (29%). In terms of a sensitivity analysis, these latter terms are both dominating the final error. On a logarithmic scale, this results in an uncertainty ∆ log Ki snow surface/air of 0.25 for all compounds (see eq 5), which is 6-12% of log Ki snow surface/air. Comparison with Literature Values. For some compounds, our results can be compared with literature data (2, 28, 30), after extrapolation to our experimental temperature of -6.8 °C (see Table 2 in Supporting Information). Extrapolation assumes that ∆Hi snow surface/air is constant over that range. Literature values for n-octane, tetrachloroethene, chlorobenzene, and 1,4-dichlorobenzene were taken from ref 2. They were measured with IGC-FID on frozen water-coated Chromosorb P. Comparison shows that our values for these nonpolar compounds are a factor 6-16 higher. This is clearly out of the range of our uncertainty and cannot be readily explained. Uncertainty in SSAsnow is rather unlikely to explain this factor, because an SSAsnow 6-16 times higher than assumed here has only been reported in the literature for freshly fallen snow (23). One possible explanation is that the surfaces examined in the two works differ significantly in their sorption properties. The literature value for ethanol from ref 28 was measured with a flow tube coupled to mass spectrometry on frozen water-coated tubes. Comparison of that value, extrapolated to our experimental temperature directly from the range measured by ref 28 (-55 to -40 °C), shows very good agreement. This is rather astonishing, as the ice surface at those low temperatures is not expected to possess any QLL. Sorption coefficients for fresh crushed ice for n-nonane, p-xylene, chlorobenzene, 1,2-dichlorobenzene, 1,3-dichlorobenzene, anisole, ethyl acetate, and acetone as well as values on aged crushed ice for anisole, ethyl acetate, and acetone were measured with IGC-FID (30). n-Nonane, p-xylene, chlorobenzene, 1,2-dichlorobenzene, and 1,3dichlorobenzene exhibited the same adsorption coefficients on fresh and aged ice (30). Those values and the value of anisole on freshly pounded and on aged ice were in the same range as our values. Values for ethyl acetate and acetone on fresh ice were an order of magnitude lower than in this work. The values for the polar compounds on aged ice differed even more than the values on freshly pounded ice, by a factor of 32 for ethyl acetate and 20 for acetone. One may have expected the opposite, that is, aged ice values would be closer to the snow values measured in this work. This was expected as water molecules on the aged ice surface as well as on the aged snow surface examined here were assumed to rearrange to a thermodynamically favorable state. This state should be similar for both surfaces. The observed agreement/disagreement for nonpolar and polar compounds between our experimental data and those of Hoff or Goss is also found if we extrapolate our data with eq 5 (see Sorption Model Based on Molecular Interactions) to compounds that were only measured by Hoff or Goss but not in our work. Uncertain assumptions for the SSAsnow would have an effect on all compounds, shifting the disagreement between values reported here and in ref 30 from the polar to the nonpolar VOL. 38, NO. 15, 2004 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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FIGURE 1. Plot of the experimental sorption coefficient log Ki snow surface/air (m3/m2) at -6.8 °C vs the logarithmic vapor pressure log p°L (Pa) (56), corrected to -6.8 °C. n ) 53, r 2 ) 0.11. compounds. While it remains unclear where the discrepancy comes from, we believe that the data presented here are the most representative for environmental condition because they were obtained from a real snow sample. Comparison with Adsorption to a Subcooled Liquid Water Surface. Various authors have suggested that adsorption to snow or ice can be estimated by extrapolating adsorption on the surface of liquid water to temperatures below 0 °C (2, 3, 16). This has been justified by the existence of the QLL. To evaluate this hypothesis, we compare snow surface sorption to extrapolated water adsorption. Water surface adsorption coefficients (Ki water surface/air) at 15 °C were taken from our previous work (36). The enthalpies ∆Hi water surface/air needed for an extrapolation of Ki water surface/air from 15 to -6.8 °C were estimated from an empirical correlation between ∆Hi water surface/air and the respective ln Ki water surface/air (55), assuming the enthalpies to be constant in this range. Two equations were derived to calculate ∆Hi water surface/air from ln Ki water surface/air, one for alkanes (∆Hi water surface/air (kJ/mol) ) -5.18 ln Ki water surface/air (m) 108) and one for all other compounds (∆Hi water surface/air (kJ/mol) ) -5.52 ln Ki water surface/air (m) - 107). The ratios of Ki snow surface/air and Ki water surface/air as well as calculated ∆Hi water surface/air are given in Table 1. Sorption of nonpolar compounds to the snow surface is strongly underestimated by the extrapolated water surface values (factor of 1-19), and sorption of polar compounds lies in the same range (factor of 1-5). Again, a wrong SSAsnow would have an effect on all compounds and would only shift the disagreement between the compounds but not explain them. Extrapolation of adsorption to the water surface involves an error up to an order of magnitude. Therefore, sorption to snow cannot simply be explained by adsorption to a subcooled water surface. Sorption Model Based on Molecular Interactions. As already addressed in the Introduction and illustrated by Figure 1, no correlation exists between log Ki snow surface/air and log p°L (56) for the wide range of compound classes measured in this work. Note that similarly poor correlation is obtained with the octanol/air absorption coefficient Ki oa (R 2 ) 0.25, data not shown). Hence, we had to find a better prediction tool. In our previous work on adsorption to surfaces, eq 1 had been proven useful for the understanding and prediction of the experimental data of a diverse set of compounds and surfaces (34-36). Others have shown that absorption data into a bulk phase can be explained by an equation similar to eq 1, but using five terms: ∑Ri, ∑βi, the 4082

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FIGURE 2. Plot of the log Ki snow surface/air (m3/m2) calculated from eq 5 vs the experimental log Ki snow surface/air (m3/m2) at -6.8 °C. n ) 57, r 2 ) 0.90. molar volume, a molar refraction term (R2), and a dipolaritiy/ polarizability term (πH 2 ) (41, 57). For our snow data, we found that eq 1 gave the better fit. The model for surface adsorption (eq 1) yields the following equation when used to fit our experimental data to describe sorption of organic vapors to snow at -6.8 °C (n ) 57, R 2 ) 0.90):

log Ki snow surface/air ) 0.639((0.037) log Ki hexadecane/air +

∑β + 3.53((0.25)∑R - 6.85

3.38((0.17)

i

i

(5)

The values calculated with eq 5 plotted against experimental values are shown in Figure 2. Note that only 57 compounds reported in Table 1 were included in the fit because compound parameters were not available for tert-amyl methyl ether (TAME), tert-butyl ethyl ether (ETBE), and tertbutyl format. Butyronitrile was statistically identified as an outlier (taking residuals vs fitted values, normal Q-Q plot, scale-location plot, and Cook’s distance plot into account). The influence of values of single compounds and of combinations of compounds on eq 5 was examined, and no other compound was found to be an outlier. Results of experiments on two columns have been merged into one data set for the evaluation with the adsorption model. As can be seen in Figure 2, the two single sets are well described by one model equation (eq 5). Comparison of net retention volumes of the same compounds on the two columns shows some discrepancies. Retention volumes on column 1 are 3050% lower than on column 2. For most test compounds (1,1,2,2-tetrachloroethane, chlorobenzene, acetone, and ethanol), these differences lie within the total uncertainty of the experimental results and the model, except for n-nonane on column 2. Those values were discarded. We used all data in one set, as evaluation of the single column sets did not yield significantly different model results. While eq 5 looks similar to the equation we used for our adsorption data earlier (34-36), it still differs significantly in the value of the constant. The constant in eq 1 (const) is given by the standard state for adsorption as described in ref 36 and has the value of -8.47 at 15 °C. This value has been confirmed for all surfaces we examined, within a deviation of 0.3 log units (34-36). However, the constant derived in eq 5 is not consistent with the underlying adsorption theory, as it does not approach the theoretical value (-8.48 at -6.8 °C). The difference between the expected and the derived value leads to a factor of 43 of stronger sorption than expected

from the fitted snow surface parameters for all compounds. This could theoretically result from a wrong approximation of the SSAsnow, which would then be 1.51 m2/g. Such a high SSAsnow has not even been found for freshly fallen snow (23). As described in section Specific Surface Area of Snow Samples, the snow layer sampled is too old to have a significantly higher SSAsnow value. As mentioned in ref 23, artifacts caused by formation of amorphous ice can lead to higher SSAsnow. In the field, formation of rime with a high SSAsnow by condensation can be excluded, as columns were precooled in the portable freezer, where the relative humidity (RH) should be similar to ambient RH. This should not lead to condensation of H2O on the columns. In the laboratory, it can be assumed that the snow crystals were thermodynamically stable during the time of the experiments, so that no rearrangement could occur. Condensation in the IGC system can be excluded as well, as the temperatures of the snow filled tube for equilibration of the carrier gas (-7.0 °C) and the snow-packed column (-6.8 °C) were chosen to prevent condensation and evaporation of H2O from the column (99.7% RH). As the system did not show any leaks, no ambient air with high absolute H2O contents could enter the system either. Since there has been evidence of the special character of the ice surface and the possible existence of a QLL on ice, we compared our data to absorption in a subcooled liquid water film. This comparison is rather speculative, because the QLL is not believed to behave as a bulk liquid (e.g., refs 17, 42, 45, 47, and 48). Nevertheless, it is taken as a first approximation to enlighten another aspect of our results. Air/bulk water partitioning coefficients Ki H (41) and enthalpies ∆H °iS for the air/bulk water transfer (from data compilation in ref 58) for 29 compounds in our data set were found in the literature. Ki H values were extrapolated to -6.8 °C. The retention volume (Vi net) expected from partitioning into this hypothetical, thin, subcooled liquid water film is calculated from °C Vi net ) K -6.8 VQLL iH

(6)

For the calculation of the volume (VQLL) of the hypothetical QLL, the compilation of experimental data and the range of QLL thickness in ref 17 was used (p 246 therein): 0.1-50 nm. For film thickness of 0.1 and 2 nm, only 1-14% of the experimental retention volume could be explained by absorption. But for a film thickness of 10 nm, solution in the hypothetical QLL could contribute 20-70% of the retention volume for 6 of the 29 compounds. At a film thickness of 50 nm, even 16 out of 29 compounds could have had a contribution >20%, and of those, 10 compounds could be dominated by the QLL absorption. Ions incorporated in snow can lower the freezing point of the snow and possibly lead to a thicker QLL. Concentrations of major ions have been measured, but the methods used were not precise enough, and only an upper limit of 7.4 × 10-4 M for the ion strength of the meltwater could be determined. It remains unclear what relevance a diffusion process along grain boundaries and into veins and nodes (as described for inorganic gases by 46) could have for organic vapors. In any case, this would be a slow mechanism that we would not have observed in our experiments. Therefore, our data are rather unlikely to be influenced by this mechanism. The “surface” parameters a-c for snow in eq 5, generated with a multiple regression analysis of the data set, are descriptors for the overall sorption at the air/snow interface, as it remains unclear which processes are contributing to this sorption (see above). In addition, they cannot be compared to other surface parameters, as they are temperature dependent and all other surfaces in our work were examined at 15 °C (32, 34-36).

We cannot clearly identify the underlying physical processes dominating sorption to snow on the molecular level (adsorption to the snow crystal surface, incorporation in the solid ice crystal, absorption into a QLL, or grain boundary/ vein effects). Nevertheless, we have presented a valuable predicting tool with eq 5. The total set of Ki snow surface/air can be well described with this equation (R 2 ) 0.90). For the inclusion of sorption to the snow surface in pollutant fate models, eq 5 can be applied to compounds not measured in this work. For this, the compound parameters used in this work have to be known for the compound of interest. The model is limited to the temperature used in this work and to equilibrium conditions and represents a first approximation because it is based on one snow sample. To our knowledge, the presented sorption model represents the only one based on such a broad set of compounds so far. It is also the first approach to understand the van der Waals and the hydrogen-bond interactions of snow on this broad basis. To allow a more general statement on sorption properties of snow, the temporal and spatial snow variability as well as the influence of particles have to be investigated. Further measurements should include the analysis of the SSAsnow as well as the dependency on temperature. For the theoretical understanding of the various sorption processes to snow, measurements should be conducted on a pure ice surface, as has been done by refs 2 and 30, but with a broad compound set as presented in this work.

Acknowledgments We would like to thank Chris Pielmeier (SLF) for fruitful discussions of snow sampling and meteorological data; Christian Niederer (EAWAG) and Silvan Kappeler for help at sampling; Torsten Schmidt (University of Tuebingen) and Dwane Paulsen (PSI, Villigen) for critical comments on the manuscript.

Supporting Information Available Meteorology of sampled snow layer including four figures and comparison with literature values including a table. This material is available free of charge via the Internet at http:// pubs.acs.org.

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Received for review September 29, 2003. Revised manuscript received March 11, 2004. Accepted May 24, 2004. ES0350684