Environ. Sci. Technol. 2002, 36, 2701-2707
Estimated Effects of Composition on Secondary Organic Aerosol Mass Concentrations FRANK M. BOWMAN* AND ANA M. KARAMALEGOS Department of Chemical Engineering, Vanderbilt University, Nashville, Tennessee 37235
Mt (µg m-3), present in the aerosol phase, with m-3 referring in all cases to a volume of gas (11)
Ki )
Introduction Secondary organic aerosol (SOA) can be a major contributor to fine particulate levels in both urban and rural atmospheres (1-3). SOA is formed when hydrocarbons are oxidized in the atmosphere creating semivolatile products that condense into the aerosol phase. Organic aerosol is part of a complex atmospheric system with hundreds of different hydrocarbons, both natural and anthropogenic, acting as aerosol precursors, most producing dozens of individual aerosol products (46). Secondary organic aerosol products are concentrated in the fine particulate size range and influence a variety of atmospheric processes including visibility reduction (7), cloud droplet formation (8), and adverse health effects (9). SOA formation has been shown to occur via an absorptive partitioning mechanism, where gas-phase semivolatile products partition into an existing aerosol phase of absorbing material (10, 11). Absorptive partitioning of a semivolatile compound i is described by a partitioning coefficient, Ki (m3 µg-1), which is the ratio of the aerosol-phase concentration, Ai (µg m-3), and the gas-phase concentration, Gi (µg m-3), normalized by the total concentration of absorbing material, * Corresponding author phone: 615-343-7028; fax: 615-343-7951; e-mail:
[email protected]. 10.1021/es015717g CCC: $22.00 Published on Web 05/10/2002
2002 American Chemical Society
(1)
The absorptive partitioning coefficient can be expressed as a function of physical and thermodynamic properties of the semivolatile compound (12)
Ki )
The composition dependence of secondary organic aerosol (SOA) mass concentrations is explored using an absorptive partitioning model under a variety of simplified atmospheric conditions. A thermodynamic model based on the Wilson equation is used to estimate activity coefficients for mixtures containing primary organics, secondary organics, and water. Changes in the mean molecular weight of the absorbing aerosol mixture due to the presence of water are also accounted for. Model simulations use semivolatile and primary organic components with differing affinities for each other and for water. Results suggest that aerosol composition has an effect on SOA levels that is significant and comparable in magnitude to those due to diurnal temperature variations and semivolatile precursor emission rates. For dissimilar organic components at zero relative humidity, predicted peak SOA mass concentrations are reduced up to 45%. Under low and high relative humidity conditions, SOA levels increase by 3-41% and 11-130%, respectively, depending on the hydrophobicity of the organic components, with maximum concentrations at night when humidity is highest. Effects are most pronounced for relatively volatile components that are most sensitive to shifts in the amount and type of absorbing aerosol.
Ai Gi M t
10-6 RT MWζipi°
(2)
where R is the ideal gas constant (8.206 × 10-5 m3 atm mol-1 K-1), T is temperature (K), MW is the mean molecular weight of the absorbing aerosol phase (g mol-1), ζi is the activity coefficient of species i in this phase, pi° is the vapor pressure of product i as a pure liquid (atm), and 10-6 is a conversion factor (g µg-1). Ki values are not constant but vary with temperature and aerosol composition (13-19). In addition to the temperature term represented explicitly in eq 2, vapor pressure is highly dependent on temperature. Activity coefficients are also temperature-dependent, but this effect is negligible compared to the exponential temperature dependence of vapor pressure. The composition of the absorbing aerosol mixture affects the partitioning coefficient through the activity coefficient and the mean molecular weight. While absorptive partitioning theory initially assumed that absorbing matter was composed entirely of organic compounds, theoretical, experimental, and field data suggest that water and perhaps other inorganic compounds may also be important components of the absorbing mixture (19-22). Thus, experimentally measured partitioning coefficients determined at a single temperature and composition are likely inadequate for representing organic partitioning behavior under varying ambient atmospheric conditions. In this paper, we describe a preliminary investigation of the effect of composition on SOA partitioning in the atmosphere. Temperature effects were the focus of a previous study (23), and this is an extension of that work. We use a simple thermodynamic model to estimate compositiondependent activity coefficients, ζi, for mixtures containing primary organics, secondary organics, and water together with a simplified model system that represents typical atmospheric conditions. Model results are used to show that, like diurnal temperature fluctuations, aerosol composition will likely have a significant effect on the partitioning behavior of SOA and resulting atmospheric mass concentrations of SOA.
Modeling Approach The modeling framework used is similar to that of Sheehan and Bowman (23) and is summarized here. For convenience, the multicomponent aerosol mixture is modeled with four components. Semivolatile organic compounds are represented by two surrogate products, S1 and S2, formed by the simplified reaction of a HC precursor with OH radical
HC + OH f R1S1 + R2S2
(3)
S1 represents products that are relatively nonvolatile and tend to absorb into the aerosol phase, while S2 represents more volatile products that are absorbed to a lesser degree. A nonvolatile organic compound, POA, is used to represent primary organic aerosol that remains entirely in the aerosol VOL. 36, NO. 12, 2002 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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phase and contributes to the absorbing aerosol medium. Water (H2O) is treated as a semivolatile species that can partition into the absorbing aerosol phase. It is important to note that the model compounds S1, S2, and POA are idealized surrogates for organic aerosol mixtures containing hundreds of individual components. As such, their properties do not correspond directly to specific real compounds but are “average” values meant to be representative of typical multicomponent mixtures. Additional oxidation pathways, such as reaction with O3 or NO3, are not included in this simplified model (23). Vapor Pressure. Temperature changes have the greatest effect on Ki through the exponential temperature dependence of vapor pressure. In our model, vapor pressures for S1 and S2 follow the Clausius-Clapeyron equation (24)
pi° ) Bi exp
( ) -Hi RT
(4)
where Bi is the pre-exponential constant of product i (atm) and Hi is the enthalpy of vaporization of product i (kJ mol-1). POA is nonvolatile and is assumed to have a vapor pressure of zero with no temperature dependence. The vapor pressure of water is calculated with an empirical expression developed by Richards (25)
pH2O° ) exp(13.3185Tr - 1.976Tr2 - 0.6445Tr3 0.1299Tr4) (5) where Tr ) (T - 373)/T. Molecular Weight. Changes in composition influence partitioning behavior in part due to the mean molecular weight term in Ki (19). The mean molecular weight of the absorbing aerosol phase, MW, is calculated as a weighted average of the mixture component molecular weights, MWi,
MW )
∑
xi MWi
(6)
i
where xi is the mole fraction of component i. Organic aerosol components, being sufficiently large that they can partition out of the gas phase, tend to have relatively high molecular weights (MWi > 150) (15, 18). For aerosol mixtures of similar organic compounds, the average molecular weight varies little with composition (18). When the absorbing aerosol contains compounds with different molecular weights, however, MW is strongly dependent on composition. This is most evident in the case of water (MWi ) 18), which has the potential to significantly lower the mean molecular weight, thereby increasing partitioning to the aerosol phase (19). Activity Coefficient. Aerosol composition also controls activity coefficient values. Organic aerosols in the atmosphere range from nonpolar long-chained alkanes found in diesel exhaust to the polar acids of SOA (15, 26, 27). Theoretical predictions based on experimental measurements of organic aerosol partitioning have been used to estimate the activity coefficients of various organic aerosol components (15, 18, 19, 27). Jang et al. (15) found ζi values that ranged between 0.3 and 198 or between 1.1 and 98.8, with values greater than 10 occurring only for mixtures of highly dissimilar compounds such as long-chained alkanes in polar wood smoke aerosol. Infinite dilution activity coefficients of water varied from near 1 for wood soot aerosol to 2.4 for R-pinene/O3 SOA to 32 for diesel soot aerosol (27). Pankow et al. (18) predicted similar ranges for secondary aerosols formed from several biogenic terpenes and cyclohexene with values typically between 0.3 and 3.0 at low relative humidity (RH). Activity coefficients for R-pinene/O3 products were near unity (0.81.2) and varied only slightly with RH when RH < 70% (19). 2702
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TABLE 1. Relationship between Λij and ζi,inf Λij ζi,inf
0.1 25
0.2 11
0.5 3.3
0.7 1.9
1.0 1.0
1.3 0.57
The activity coefficient of water was approximately 1.7-1.8 for the R-pinene/O3 aerosol and 1-1.2 for the cyclohexene/ O3 aerosol (19). Thus, while activity coefficients may vary little with composition for a mixture of similar compounds, they are expected to vary significantly when the mixture contains dissimilar compounds. A variety of methods are available for modeling activity coefficients ranging from assumed ideality where all ζi ) 1, to simple correlations such as the Margules or Wilson equations which use 1 or more molecular interaction parameters, to more complex group contribution methods such as UNIFAC involving multiple parameters and which require detailed composition information (28). In this study, where aerosol composition has been highly simplified to include only three surrogate organics and water, the Wilson equation is used. It provides sufficient detail to account for the compositional dependence of activity coefficients without requiring additional parameters for which information is unavailable. It has also been found to perform well at representing a wide variety of miscible binary or multicomponent mixtures (28). The multicomponent Wilson equation takes the following form (28):
ln(ζi) ) 1 - ln
xjΛji
∑x Λ - ∑ ∑x Λ j
j
(7)
ij
j
k
jk
k
where xj is the mole fraction of component j, Λij is a parameter representing the interaction between compound i and compound j, and all summations are over all species. For simplicity, component interactions are assumed symmetrical (Λij ) Λji). The relationship between Λij and ζi,inf values is shown in Table 1. ζi,inf is the activity coefficient of compound i at infinite dilution in a binary solution with compound j and is calculated as a function of Λij. When Λij ) 1, the attraction between molecules of compounds i and j is the same as for the molecules in their pure liquid state, ideal behavior occurs, and ζi ) 1. When Λij < 1, molecules have a weaker attraction to each other leading to activity coefficients greater than unity which will result in lower Ki values. When Λij > 1, molecules show a greater attraction, activity coefficients are less than unity, and Ki increases. For most mixtures, Λij < 1. ζi reaches a maximum (or minimum if Λij > 1) at infinite dilution. At higher concentrations, ζi decreases approaching unity as xi f 1. Partitioning Coefficient. Experimentally measured partitioning coefficients are generally determined at a constant temperature and with a chemically similar absorbing mass. These experimental Ki* values represent partitioning at a reference temperature, T*, under ideal solution (ζi ) 1) and constant molecular weight, MW*, conditions. When combined with eqs 2 and 4, a temperature- and compositiondependent partitioning coefficient for semivolatile organics can be defined
Ki (T, xi) ) Ki*
( )( )( ) [ (
Hi 1 1 MW* 1 T exp MW ζi T* R T T*
)]
(8)
where MW and ζi are defined by eqs 6 and 7, respectively. Parameters needed to model temperature- and composition-
TABLE 2. Base Case Model Parameters parameter peak [OH] (ppt) advection loss rate (% h-1) primary organic aerosol (µg m-3) HYA emissions (ppb h-1) kOH,298 K (ppm-1 min-1) R1 R2 MW* T* (°C) temperature (°C)
TABLE 3. Aerosol Component Properties base case value
component
Ki* (m3 µg-1)
0.1 2.88 10 2.25 1.36 × 104 0.071 0.138 150 35 25 ( 10
S1 S2 POA H2O
0.053 73 0.0019 73 1012 0 calculated from vapor pressure equation
FIGURE 1. Daily profiles for relative humidity (%), temperature (°C), and OH radical concentration (×10-8 ppm). dependent absorptive partitioning using eq 8 are T*, MW*, Ki*, Hi, Λij, and MWi. Model Simulations. A single-cell box model was used to simulate the reaction of an emitted hydrocarbon with OH radical and subsequent aerosol formation from semivolatile products. Base case model parameters are summarized in Table 2. As in the previous study, high yield aromatics (HYA), which include compounds such as toluene, ethylbenzene, and ethyltoluene, were used as the SOA precursor. Stoichiometric coefficients, constant temperature partitioning coefficients, and OH rate constant are those used in Sheehan and Bowman (23) and are based on available literature data for HYA (29, 30). A fixed diurnal OH concentration profile, as shown in Figure 1, was used to maintain constant conditions from day to day. Aerosol formation was assumed to occur exclusively via absorptive partitioning into existing primary organic aerosol. Aerosol surface area was sufficient to ensure rapid gasaerosol transport such that equilibrium conditions were approximated in all simulations. Constant precursor emissions and continuous advection of clean air into the box established a repeating daily steady state. POA was emitted at a constant rate and maintained a constant concentration of 10 µg m-3 due to advective dilution. Total organic aerosol concentrations in the base case simulation average ∼20 µg m-3, consistent with measured daily average organic carbon concentrations (31), with SOA contributing approximately 50% (1-3). The model was run for a 10-day period, with daily aerosol profiles becoming stable after approximately 5 days. A fixed daily temperature profile with a range of 25 ( 10 °C (see Figure 1), similar to summertime temperatures in the southern United States (32), was used to model the effect of temperature fluctuations on partitioning behavior. Temperature effects on the HYA-OH rate constant and other reactions that influence the concentration of OH were
Hi (kJ mol-1)
MWi 150 150 150 18
TABLE 4. Model Simulation Parameters RH
ΛSV-POA
0 0 0 low, high low, high low, high low, high 0 0
1.0 1.0 0.7 0.7 0.7 0.7 0.7 0.5 0.2
ΛH2O-POA
ΛH2O-SV
(T ) 25 °C ) constant) 0.1 0.2 0.5 0.7
0.2 0.5 0.7 1.0
neglected, based on previous model results (23), such that temperature variations did not alter the rate of semivolatile product formation. As a result, in each model simulation, the same total amount of semivolatile product is available to partition between the gas and aerosol phases. Partitioning properties for the four aerosol components are reported in Table 3. Ki* values for S1 and S2 are those reported by Odum et al. (29), while the Hi values for these two compounds were assumed to be 73 kJ mol-1 based on previous estimates (23). POA was treated as nonvolatile and was therefore assigned a very large Ki* value. Molecular weights for all of the organics were assumed equal to the reference molecular weight, MW*, of 150. This value was estimated based on the mean molecular weights of several typical organic aerosol mixtures, which range from 118 to 249 (15, 18). H2O has a much lower molecular weight of 18. As a result, the mean molecular weight, MW, in these simulations will vary only when aerosol water is present. For H2O, the partitioning coefficient was calculated directly using the vapor pressure eq 5 and the activity coefficient of water in eq 2. Differences between compounds in the aerosol mixture are represented in the model by the Λij parameter in eq 7. Aerosol compounds are assigned into three different groups: SV, composed of S1 and S2 which are assumed to be chemically similar; POA; and H2O. To characterize the interactions between these three groups, three Λij parameters (ΛSV-POA, ΛH2O-POA, ΛH2O-SV) are defined for each simulation. To investigate the effect of composition on organic aerosol partitioning, two sets of model simulations were run using different SV and POA types under different relative humidity (RH) conditions. Table 4 outlines the RH and Λij parameters used in these simulations. The set of Λij values used was selected to give organic aerosol activity coefficients in the range of 1-4, and water activity coefficients in the range of 1-8, consistent with previously discussed estimates (15, 18, 19, 27). The first set of simulations held RH ) 0, where only the SV-POA interaction is relevant. ΛSV-POA was varied to represent S1 and S2 partitioning into a POA that ranged from being identical in nature to SV to being highly dissimilar. A constant temperature simulation was also run at zero humidity to establish a baseline for the magnitude and timing of temperature effects. A second set of simulations was performed under humid conditions, with the organics varying in nature from hydrophobic to hydrophilic. Both high and low humidity profiles were explored. Figure 1 shows the RH profile for the high VOL. 36, NO. 12, 2002 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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FIGURE 2. Daily profiles for total (gas + aerosol) phase mass concentrations of POA, S1, S2, and HYA. HYA values shown have been multiplied by 0.2. humidity case, cycling between 55% during the day and 84% at night. The low humidity profile is identical except that it is lower by 40%, cycling between 15% and 44%. RH profiles are based on average RH data for various U.S. cities (32). The high humidity profile is typical of locations throughout the United States, while the low humidity profile is similar to summertime conditions in the southwest. While the model parameters used, as well as the organic aerosol concentrations predicted by the model, are consistent with available experimental and field data, it should be emphasized that this model system is highly simplified and is not meant to precisely represent actual atmospheric conditions. Actual ambient concentrations in a polluted environment are unlikely to reach a repeating steady state. Precursor emissions will come from a variety of sources and will vary with time of day leading to varying amounts and types of available condensable material. Other complicating effects, such as deposition, coagulation, and gas-phase chemistry variations due to temperature, and relative humidity, have also been neglected. Atmospheric aerosols are highly complex mixtures, containing organic and inorganic components, as well as water. This simplified model makes no attempt to fully represent all of the different possible interactions but focuses solely on a miscible organic-water mixture. The model provides a controlled scenario within which the effects of aerosol composition on partitioning can be evaluated. Model results should not be interpreted, therefore, as a direct prediction of atmospheric behavior but as estimates of the relative magnitude and nature of composition effects.
Results and Discussion Base Case Simulation. The base case parameters listed in Table 2 were used in all simulations and resulted in the same total mass concentrations of organic compounds for each simulation. Figure 2 shows the total combined gas and aerosol phase mass concentrations of HYA, POA, S1, and S2 for days 9 and 10 of the simulations, when a constant repeating daily cycle has been established. HYA is emitted at a constant rate and builds up at night when OH radical concentrations are zero and no oxidation reaction occurs. Once OH levels rise during the day, HYA reacts and its concentration drops. As a result, HYA concentrations peak at approximately 7:00 a.m. and reach a minimum near 4:00 p.m. The semivolatile products S1 and S2 follow the reverse cycle, reaching maximums of 35 µg m-3 and 18 µg m-3 in the afternoon and minimums of 24 µg m-3 and 12 µg m-3 in the morning. POA is emitted at a constant rate but does not react, reaching a constant concentration of 10 µg m-3 when emitted POA is equal to that lost by dilution. 2704
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FIGURE 3. Daily organic aerosol mass concentrations at RH ) 0 for ΛSV-POA ) 1.0, 0.7, 0.5, and 0.2. The cycle of S1 and S2 shown in Figure 2 determines how much organic material is available for partitioning between gas and aerosol phases and is one of three key cycles governing SOA levels. The other two cycles are temperature and relative humidity (see Figure 1). Temperature influences partitioning coefficient values and in these simulations it peaks at 3:00 p.m. and reaches a minimum at 4:00 a.m. RH is a function of both water vapor concentration and temperature and acts as a measure of how much water will partition to the aerosol phase. It varies nearly inversely with temperature, reaching a maximum in the early morning and a minimum in the afternoon. It is important to note that the timing of these peaks is specific to this simplified model. In a real atmosphere, precursor emission rates are not constant but will vary throughout the day due to vehicle use, manufacturing schedules, temperature and sunlight-driven vegetative emissions, and other factors. Temperature and relative humidity profiles will be different and can be expected to vary considerably from day to day. As a result, semivolatile concentrations are likely to be highly variable and much different from this scenario. Again, these simulations are not intended as a direct representation of specific atmospheric conditions but as a general model for assessing the importance of compositional variations on SOA levels relative to the effects of temperature and semivolatile concentrations. Predicted organic aerosol concentrations under zero humidity conditions are shown in Figure 3. The dotted line corresponds to the constant temperature simulation where only the semivolatile cycle has an influence on time-varying SOA mass concentrations. In this case, SOA mass mirrors semivolatile concentrations, with a peak in midafternoon and a minimum in the morning. When a variable temperature profile is used, a second time cycle is introduced, and the predicted SOA mass is modified as seen with the solid line in Figure 3. Here, the afternoon peak is suppressed due to high temperatures, and nighttime mass concentrations increase due to low temperatures. This change in the magnitude and timing of peak SOA levels due to temperature variations was explored more thoroughly in the previous study (23) and will not be repeated here. For this study, the simulation results calculated assuming ideal solution behavior (solid line in Figure 3 where ζi and Λij values are all unity) provide a background profile against which compositional changes can be compared. SV-POA Interactions. When no water is present, the only component interaction is between the SV group and POA. Different ΛSV-POA values were used to represent semivolatiles being absorbed into different types of POA. Simulation results of predicted organic aerosol mass concentrations are shown in Figures 3 and 4 for ΛSV-POA ) 1.0, 0.7, 0.5, and 0.2. ΛSV-POA
FIGURE 4. Daily aerosol component mass concentrations at RH ) 0 for ΛSV-POA ) 1.0, 0.7, 0.5, and 0.2: (a) semivolatile component S1, (b) semivolatile component S2. ) 1.0 represents one extreme where POA and SV are identical in nature, while ΛSV-POA ) 0.2 corresponds to POA and SV that are highly dissimilar. ΛSV-POA ) 0.7 and 0.5 correspond to moderately dissimilar compounds and are meant to be representative of typical atmospheric conditions. As the components become more dissimilar, coexistence in the aerosol phase is less favorable and organic aerosol mass decreases. Because POA is nonvolatile and remains constant, the changes with ΛSV-POA shown in Figure 3 are due entirely to a reduction in the amount of SOA formed. For even mildly dissimilar components, there is a noticeable compositional effect on predicted SOA mass concentrations. The peak SOA level in Figure 3 for ΛSV-POA ) 0.7 is reduced by 12% as compared to the ideal case (ΛSV-POA ) 1.0). The volatile product S2 is more sensitive to compositional differences than total SOA, which is dominated by S1, with peak S2 aerosol mass concentrations in Figure 4b decreasing by 20% for ΛSV-POA ) 0.7. Lower ΛSV-POA values result in even greater reductions of 23-45% of total SOA and 35-64% of S2 aerosol. For ΛSV-POA ) 0.7 and 0.5, the decrease in mass concentration is relatively uniform with time of day. For ΛSV-POA ) 0.2, however, concentrations are more sensitive to temperature, and the low-temperature-induced nighttime peak dominates over the afternoon semivolatile peak. The cause of this shift in partitioning behavior can be seen by examining the individual semivolatile components. The total (gas + aerosol) mass concentration of S1 (as shown in Figure 2) cycles between 18 and 12 µg m-3 so that 52-63% of S1 partitions to the aerosol phase when ΛSV-POA ) 1.0. Contrast this with S2, which is more volatile and is a much smaller
FIGURE 5. Daily activity coefficient values at RH ) 0 for ΛSV-POA ) 1.0, 0.7, 0.5, and 0.2: (a) POA, (b) SV. contributor to SOA levels. Almost all of the S2 present remains in the gas phase, with only 4-6% of the 24-35 µg m-3 total S2 existing as aerosol. As ΛSV-POA values increase, S2 concentrations also increase, but the shape of the S2 aerosol curves in Figure 4b, where the temperature cycle has a greater influence than the semivolatile cycle, is relatively unchanged. For S1 (Figure 4a), similar behavior is seen at ΛSV-POA ) 0.2, but at higher ΛSV-POA values, a greater fraction of S1 exists as aerosol and there is less gas-phase S1 available to shift to the aerosol phase when temperatures decrease. As a result, the low-temperature peak becomes less prominent as the fraction of S1 in the aerosol phase increases. Figure 5 shows how the activity coefficients of the organic aerosol components vary with ΛSV-POA. Because both S1 and S2 are treated as the same compositional type, their activity coefficients have the same values. At ΛSV-POA ) 1, activity coefficients are by definition 1.0, regardless of aerosol composition. As ΛSV-POA decreases, activity coefficients for both POA and SV increase with average values of 1.1, 1.2, and 1.3 for POA and 1.2, 1.5, and 2.6 for SV. In most simulations, there is always more POA than SV in the aerosol phase so that the aerosol mixture tends to resemble POA. As a result, ζPOA values are lower than those for SV. Figure 5 also illustrates that activity coefficients have a compositional dependence independent of ΛSV-POA. Because of changing SV mass concentrations, the relative concentrations of SV and POA in the organic aerosol mixture also change with time of day. When SV mass reaches a minimum in the morning and the organic aerosol is mostly POA, ζSV values peak. At night, when SV levels are higher and the aerosol mixture is more similar to SV, ζSV values reach a minimum. ζPOA values show the reverse trend, reaching a minimum in the morning when the POA fraction is high and a maximum at night when the mixture contains relatively less POA. VOL. 36, NO. 12, 2002 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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FIGURE 6. Daily organic aerosol mass concentrations for ΛSV-POA ) 0.7 and ΛH2O-POA/ΛH2O-SV combinations of 0.1/0.2, 0.2/0.5, 0.5/0.7, and 0.7/1.0: (a) low RH (15-44%), (b) high RH (55-84%). Humidity Effects. A second set of simulations included H2O at both low (15-44%) and high (55-84%) relative humidity conditions (see Table 4). In all cases, ΛSV-POA remained fixed at 0.7, while ΛH2O-POA and ΛH2O-SV values were varied, with POA being more hydrophobic than SV. At one extreme, ΛH2O-POA ) 0.1 and ΛH2O-SV ) 0.2, the organics are hydrophobic, while at the other, ΛH2O-POA ) 0.7 and ΛH2O-SV ) 1.0, they are hydrophilic. Intermediate values represent moderately hydrophobic organic components that are a reasonable approximation of atmospheric conditions. Atmospheric aerosols contain both organic and inorganic components which can interact with water in different manners. This simplified model makes no attempt to fully represent all of the different possible interactions and focuses solely on a miscible organic-water mixture. Figure 6 shows how predicted organic aerosol mass concentrations vary with the hydrophilicity of the organic aerosol components. As the organics become more hydrophilic and water partitions to the aerosol phase, SOA concentrations increase. As explained by Seinfeld et al. (19), the presence of water in the aerosol mixture has a 2-fold effect. First, it dilutes the aerosol mixture, lowering the mole fraction of organics, which shifts the equilibrium of semivolatile organics in favor of the aerosol phase. Second, it lowers the average molecular weight of the absorbing aerosol mixture, which causes partitioning coefficient values to increase. In the low RH simulations (Figure 6a), peak SOA mass concentrations increase by up to 41% for hydrophilic organics as compared to the case where no water is present. For more hydrophobic organics, the increase in SOA is much lower. 2706
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FIGURE 7. Daily aerosol component mass concentrations for ΛSV-POA ) 0.7, ΛH2O-POA ) 0.5, and ΛH2O-SV ) 0.7: (a) S1, (b) S2, (c) H2O. In the most hydrophobic case, when ΛH2O-POA ) 0.1 and ΛH2O-SV ) 0.2, peak SOA levels increase by only 3%. It is important to note that the increases shown in Figure 6 refer only to S1 and S2 and do not include the additional water that has partitioned to the aerosol. At low RH, even a small amount of water allows a much greater amount of organic vapor to enter the aerosol phase. In the ΛH2O-POA ) 0.5 and ΛH2O-SV ) 0.7 case, shown in Figure 7, aerosol water concentrations at low humidity are at most 1 µg m-3, but this causes peak SOA concentrations to increase by over 2 µg m-3. Under high RH conditions (Figure 6b), the effect of water on SOA concentrations is more dramatic. Peak SOA mass concentrations for hydrophilic organics increase by 130%, while for the most hydrophobic organics SOA levels increase by 11%.
Differences in time-dependent partitioning behavior of the two semivolatile components are most evident under humid conditions. As shown in Figure 7b, a peak in S2 aerosol mass occurs at night in conjunction with the peak RH. This water-produced peak increases with increasing RH and is the dominant feature in the time profile of S2 aerosol. For S1, however, only a small water-produced peak is observed (see Figure 7a). The presence of water increases S1 aerosol concentrations to the point that the majority of S1 partitions to the aerosol phase at most times of day. As a result, the time profile of S1 aerosol mirrors the total mass concentration of S1 shown in Figure 2. Only in the afternoon to evening hours, when the temperature is still high and relative humidity is lower, does a larger fraction of S1 remain in the gas phase. Atmospheric Implications. These results suggest that compositional differences exert a considerable influence on SOA mass concentrations; one that is comparable in magnitude to those due to diurnal temperature variations and precursor emission rates. Compositional effects are expected to be most important when semivolatile compounds are highly dissimilar from the absorbing aerosol mass and at high relative humidities but can still be significant even for relatively similar compounds with no water present. Computer models that neglect compositional effects, assuming constant partitioning coefficients with ζ ) 1, are expected to overpredict SOA levels if water vapor is not involved in partitioning and to underpredict SOA levels when water vapor contributes to the absorbing aerosol phase. Model errors will be most significant for more volatile components that reside primarily in the gas phase, as they are most sensitive to shifts in the amount and type of absorbing aerosol. This may be of particular importance if specific organic aerosol components are of interest due to their toxicity or their use as emission source tracers.
Acknowledgments This work was supported by the National Science Foundation under Grant ATM-9985108.
Note Added after ASAP This paper was released on ASAP on 5/10/2002 with an error in eq 7. The natural log was removed from the denominator of the last term to correct the equation. The corrected version was posted 6/13/2002.
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Received for review September 25, 2001. Revised manuscript received March 21, 2002. Accepted April 3, 2002. ES015717G
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