Estimated Effects of Temperature on Secondary Organic Aerosol

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Research Estimated Effects of Temperature on Secondary Organic Aerosol Concentrations PAUL E. SHEEHAN AND FRANK M. BOWMAN* Department of Chemical Engineering, Vanderbilt University, Nashville, Tennessee 37235

The temperature-dependence of secondary organic aerosol (SOA) concentrations is explored using an absorptivepartitioning model under a variety of simplified atmospheric conditions. Experimentally determined partitioning parameters for high yield aromatics are used. Variation of vapor pressures with temperature is assumed to be the main source of temperature effects. Known semivolatile products are used to define a modeling range of vaporization enthalpy of 10-25 kcal/mol-1. The effect of diurnal temperature variations on model predictions for various assumed vaporization enthalpies, precursor emission rates, and primary organic concentrations is explored. Results show that temperature is likely to have a significant influence on SOA partitioning and resulting SOA concentrations. A 10 °C decrease in temperature is estimated to increase SOA yields by 20-150%, depending on the assumed vaporization enthalpy. In model simulations, high daytime temperatures tend to reduce SOA concentrations by 16-24%, while cooler nighttime temperatures lead to a 22-34% increase, compared to constant temperature conditions. Results suggest that currently available constant temperature partitioning coefficients do not adequately represent atmospheric SOA partitioning behavior. Air quality models neglecting the temperature dependence of partitioning are expected to underpredict peak SOA concentrations as well as mistime their occurrence.

Introduction Secondary organic aerosol (SOA) has been identified as an important contributor to fine particulate levels in both urban and rural atmospheres (1, 2). SOA is formed when hydrocarbons (HCs) are oxidized in the atmosphere creating semivolatile products that partition from the gas to the aerosol phase. Atmospheric aerosols are composed of a complex mixture of organic and inorganic compounds that originate from a variety of both natural and anthropogenic sources. Fine particulate matter (PM) contributes to many important atmospheric processes including a range of adverse health effects, visibility reduction, and cloud droplet formation. Epidemiological studies show a link between high atmospheric concentrations of fine particulate matter and increased hospital admissions and mortality, particularly among the young, elderly, or those already suffering from respiratory problems (3). Secondary aerosol formation from * Corresponding author phone: (615)343-7028, fax: (615)343-7951; e-mail: [email protected]. 10.1021/es001547g CCC: $20.00 Published on Web 04/19/2001

 2001 American Chemical Society

both natural and anthropogenic sources contributes to visibility degradation in our national parks (4). The number and composition of fine particles available to act as condensation nuclei controls cloud characteristics such as size, lifetime, optical depth, and albedo (5). Secondary organic aerosol formation has been studied in smog chamber experiments (6-9) and has recently been shown to occur via an absorptive partitioning mechanism (7, 10). 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 m3), and the gas-phase concentration, Gi (µg m3), normalized by the total concentration of absorbing organic material, Mo (µg m3), present in the aerosol phase (7)

Ki )

Ai Gi M o

(1)

The absorptive partitioning coefficient can be expressed as a function of physical and thermodynamic properties of the semivolatile compound (11)

Ki )

10-6RT MWζip°i

(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 organic aerosol phase (g mol-1), ζi is the activity coefficient of species i in this phase, p°i 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 (12-16). In addition to the temperature term represented explicitly in eq 2, vapor pressure is highly dependent on temperature. The composition of the absorbing aerosol mixture affects the partitioning coefficient through the activity coefficient and the mean molecular weight. While SOA absorptive partitioning theory initially assumed that absorbing matter was composed entirely of organic compounds, experimental and field data suggest that water and perhaps other inorganic compounds may also be important components of the absorbing mixture (17-19). Thus, experimentally measured partitioning coefficients determined at a single temperature and composition are likely inadequate for representing organic partitioning behavior over varying ambient atmospheric conditions. In this paper we describe a preliminary investigation of the effect of temperature on SOA partitioning in the atmosphere. Compositional effects are not included here but will be considered in a subsequent study. We estimate an expected range of temperature dependence for measured constant-temperature partitioning coefficients and use a simplified model system to represent typical atmospheric conditions. Model results are used to show that diurnal temperature fluctuations will likely have a significant effect on the partitioning behavior of SOA and resulting atmospheric concentrations of SOA.

Modeling Approach Due to limitations in both speciation and thermodynamic data for HC oxidation products, it is convenient to model SOA using surrogate products. The vast array of actual products formed from a HC precursor can be lumped into VOL. 35, NO. 11, 2001 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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three surrogate products, according to degree of volatilitywhere Ri is the stoichiometric coefficient of actual product

Pi, and S1, S2, and S3 are the surrogate products. S1 represents products that are relatively nonvolatile and have a high propensity to absorb into the organic aerosol medium. S2 represents products that are more volatile than S1 and are absorbed to a lesser degree. S3 represents highly volatile species that do not absorb into the organic aerosol medium but remain in the gas phase. When focusing on aerosol formation, this three-product approach can be reduced to a two-product model because only S1 and S2 contribute to SOA. The stoichiometric and absorptive partitioning parameters, Ri and Ki, for these surrogate products are a complex weighted-average of the actual product parameters. These parameters can be determined by fitting experimental yield data measured over a wide range of Mo (7). Temperature Dependence. Within the framework of an absorptive partitioning model, changes in temperature will likely impose the greatest effect on Ki due to the exponential temperature dependence of vapor pressure. According to the Clausius-Clapeyron equation (20), this dependence is represented by

p°i ) Bi exp

( ) - Hi RT

(4)

where Bi is the preexponential constant of product i (atm) and Hi is the enthalpy of vaporization of product i (kcal mol-1). Substituting eq 4 into eq 2, the temperature dependence of Ki can be expressed as

Ki )

( )

Hi RT10-6 exp MWζiBi RT

(5)

At lower temperatures, as vapor pressure decreases, Ki will increase resulting in a greater fraction of semivolatile product partitioning to the aerosol phase. The relative sensitivity to temperature will depend on the value of Hi. Numerous laboratory and field studies have confirmed a linear relationship between log(K) and 1/T for individual semivolatile compounds (12, 13, 15, 16). Given an experimentally determined partitioning coefficient, K/i at a reference temperature, T*, an equation for a temperature-dependent partitioning coefficient, Ki(T), can be developed by assuming a constant activity coefficient and mean molecular weight

[(

Hi 1 1 T Ki(T) ) K/i exp T* R T T*

)]

(6)

Thus, to model temperature-dependent absorptive partitioning three parameters, Ri, K/i , and Hi, are required for each condensable product. Activity Coefficients. Ki values will also be sensitive to the activity coefficient which is dependent on organic aerosol mixture composition. Aerosol composition varies not only with emission patterns but also with temperature due to shifts in gas-aerosol partitioning of individual components. As a result, activity coefficients for SOA mixtures are indirectly 2130

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influenced by atmospheric temperatures. This and other effects of varying activity coefficients are beyond the scope of this study and were not considered. Activity coefficients were held constant for all simulations. Precursor Compounds. High yield aromatics (HYA) and R-pinene (AP) were used as SOA precursor compounds in our modeling study. High yield aromatics, which include compounds such as toluene, ethylbenzene, and ethyltoluene, are an important aerosol-producing component of gasoline vapor and have been suggested as being representative of anthropogenic aerosol precursor emissions in general (21). R-Pinene is the most heavily emitted biogenic aerosol precursor (22). Aerosol yield and product composition data have been reported for the reaction of OH radical with both of these precursors. Literature values of two-product stoichiometric coefficients and constant-temperature partitioning coefficients for HYA and AP were used as inputs to our model (7, 21). R-Pinene can also react with O3 or NO3 to form SOA but with different products from those created by the OH reaction. For simplicity, only the OH reaction was considered in this study. Simulations including both O3 and OH reactions would be expected to result in higher SOA levels, due to more efficient aerosol formation via the O3 reaction, but the temporal and temperature variability of partitioning would be qualitatively similar to results using OH alone. NO3 concentrations typically experience a cycle opposite that of OH and O3, with peak levels at night. As a result, NO3 reactions may provide a nighttime source of SOA. Ambient NO3 concentrations tend to be highly variable, however, and in general are not well defined. The selection of these specific precursors is somewhat arbitrary and is based primarily on the availability of partitioning and aerosol product data. The results presented are not expected to be applicable to all SOA precursors. HCs that react very rapidly or that form highly nonvolatile products, such as the sesquiterpenes, may exhibit lower sensitivity to temperature relative to other atmospheric variables. The two HCs used are intended to demonstrate different types of aerosol precursors with different sources, reaction rates, semivolatile product yields, and partitioning parameters. Vaporization Enthalpy Estimates. Temperature-dependency for these precursor compounds has not been reported and was estimated based on the vaporization enthalpies for identified aerosol products of HYA and AP. Individual aerosol products of HYA (23) and AP (24) with Hi values can be found in Table 1. Temperature-dependent vapor pressure equations using three (25) or five (26) parameters to fit experimental data were available for many of the identified HYA products. The method of Schwarzenbach et al. (27) was also used to estimate vapor pressure from measured boiling point temperatures reported in the literature (26) by assuming a constant entropy of vaporization of 88 J mol-1 K-1. These vapor pressure equations were converted to the twoparameter form of eq 4 to determine Hi values. The Hi value for pinonaldehyde is that reported by Hallquist et al. (8) and for pinic acid it was derived from vapor pressure estimates in Kamens et al. (28). The Hi values obtained vary from a low of 12.0 kcal mol-1 for the HYA oxidation product benzaldehyde to a high of 22.3 kcal mol-1 for the AP oxidation product pinic acid. It is important to emphasize that the set of values used is only suggestive of the complete range in vaporization enthalpy of all semivolatile products formed by HC oxidation. Since most products are as yet unidentified and/or lack the necessary thermodynamic data, the compiled data is necessarily incomplete. While not exhaustive, it does provide a reasonable estimate of the expected range of vaporization enthalpies for SOA components. Based on the compiled data, model Hi

TABLE 1. Vaporization Enthalpy Data for Identified Aerosol Products of High-Yield Aromatics and r-Pinene identified aerosol products

parameter

estimated by five-param three-param boiling point (27) eq (26) eq (25)

peak [OH] (ppt) advection loss rate (% h-1) background aerosol (µg m-3) kOH,298 K (ppm-1 min-1) R1 R2 K1* (m3 µg-1) K2* (m3 µg-1) T* (°C) temp (°C) H1 ) H2 (kcal mol-1) HYA emissions (ppb h-1) AP emissions (ppb h-1) primary organic aerosol (µg m-3)

13.4 13.5 13.1 12.7 13.1 14.1 13.5 14.3 13.1 12.2 14.9 15.4 13.1 15.4 12.7 13.1 13.2 15.9 12.3

15.7 15.0 12.7 14.4 17.4

13.1 12.0 17.2 16.3 16.5

17.5 17.4

19.9

15.0

14.8 16.7 13.7 19.1 13.2

r-Pinene pinonaldehyde pinic acid

HYA base case

derived Hi values (kcal mol-1)

High-Yield Aromatics 2,4-dimethylphenol 2,5-dimethylphenol 2,5-furandione 2,5-hexanedione 2,6-dimethylphenol 3,4-dimethylphenol 3-methyl-2,5-furandione 4-methyl-2-nitrophenol acetophenone benzaldehyde benzoic acid dihydro-2,5-furandione m-cresol m-toluic acid o-cresol p-cresol p-tolualdehyde p-toluic acid phenol

TABLE 2. Model Simulation Parameters

18.0 (8) 22.3 (28)

values for the surrogate products of HYA and AP were set within the range of 10-25 kcal mol-1, with a base case value of 17.5 kcal mol-1. This value is somewhat higher than or similar to those reported for PAHs (15.1-24.4 (13); ∼10 (16); 6.7-11.5 (12); 4.8-7.9 (15) kcal mol-1) but lower than that used recently for mono- and dicarboxylic acids (37 kcal/mol-1 (29)). Vaporization enthalpy is also a function of temperature. For the aerosol products listed in Table 1 for which temperature-dependence data is available, Hi values vary by less than 0.4 kcal/mol over the temperature range (15-35 °C) used in this study (26). Since this variation is much less than the uncertainty between data sources for individual compounds, Hi values can be considered constant. Model Simulations. A single-cell box model was used to simulate the reaction of HYA or AP with OH radical and subsequent SOA formation from semivolatile products. A fixed diurnal OH concentration profile was used to maintain constant conditions from day to day. The rate constant for the HYA-OH reaction was a lumped average (30) of the rate constants for a mixture of high yield aromatics (toluene, ethylbenzene, ethyltoluene, propylbenzene). These individual rate constants as well as that for the AP-OH reaction were those recommended by Carter (31). Aerosol formation was assumed to occur exclusively via absorptive partitioning. Semivolatile products of the HYAOH or AP-OH reactions formed an organic aerosol layer around insoluble background aerosols. Aerosol surface area was sufficient to ensure rapid gas-aerosol 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. Background aerosols were also emitted at a constant rate and maintained a constant concentration of 65 µg/m3 due to advective dilution. 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 was used to investigate the effect of temperature fluctuations on partitioning behavior. In each simulation, Hi values were assumed to be the same for all semivolatile products. OH rate constants were fixed for a constant temperature of

0.1 2.88 65

AP base case

additional simulations

0.1 2.88 65

1.36 × 104 7.80 × 104 0.071 0.038 0.138 0.326 0.053 0.171 0.0019 0.004 35 35 25 ( 10 25 ( 10 17.5 17.5 5.5 2.4 0 0

25, 25 ( 5 0, 10, 25 2.25, 11.0 1.2, 4.8 10, 20

300 K, such that temperature variations did not alter the rate of semivolatile product formation. As a result, in each model simulation for a given precursor the same total amount of semivolatile product is available to partition between the gas and aerosol phases. In the atmosphere, temperature will also influence the rate of HC oxidation through its effect on the HC-OH rate constant and the concentration of OH. For the base case simulation, variations in the HC-OH rate constant due to temperature (( 10 °C) resulted in SOA concentrations that varied by at most 3.2% for HYA and 1.3% for AP from those predicted using temperature-independent rate constants. Additionally, OH concentrations will increase with temperature as the rates of other atmospheric reactions increase. Simulations incorporating a full atmospheric chemistry mechanism showed the total (gas + aerosol) concentration of semivolatile species increasing by ∼20% for a 10 °C increase in temperature (29). These rate effects were not included in our simulations so that the effect of temperature on SOA partitioning could be isolated. 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 have also been neglected. The model provides a controlled scenario within which the effects of temperature on organic aerosol 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 of temperature effects. Table 2 summarizes model system parameters for base case simulations and for a series of simulations run while varying one parameter at a time from the base case. Simulations were performed for both HYA and AP, with similar qualitative results. Results for HYA are presented in the following section, while AP results can be found in the Supporting Information.

Results and Discussion Yield Curves. The aerosol-forming potential of a HC precursor can be described by a SOA yield (7)

Y)

∆Mo ∆HC

(

RiKi

∑ 1+KM

) Mo

i

i

)

(7)

o

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FIGURE 1. Estimated range of high yield aromatic SOA yields as a function of organic aerosol mass at 15, 25, and 35 °C. Upper and lower values correspond to H1 ) H2 ) 25 and 10 kcal mol-1, respectively. Parameter values used are r1 ) 0.071, r2 ) 0.138, K1* ) 0.053, K2* ) 0.0019, T* ) 35 °C.

FIGURE 2. Estimated range of r-pinene SOA yields as a function of organic aerosol mass at 15, 25, and 35 °C. Upper and lower values correspond to H1 ) H2 ) 25 and 10 kcal mol-1, respectively. Parameter values used are r1 ) 0.038, r2 ) 0.326, K1* ) 0.171, K2* ) 0.004, T* ) 35 °C. where ∆HC is the amount of HC that has reacted and ∆Mo is the amount of organic aerosol created. Equation 7 was used to generate SOA yield curves as a function of total organic aerosol mass at three different temperatures as shown in Figure 1 for HYA, and in Figure 2 for AP. Ri and K/i values at 35 °C are those reported by Odum et al. (7, 21) for the two surrogate products of HYA and AP, respectively. The diamond-shaped data points in these figures are the Odum et al. experimental data from which the Ri and K/i values were derived. Ki values used at lower temperatures were calculated from eq 6 using estimated values of Hi. The upper and lower curves for a given temperature in Figures 1 and 2 represent the estimated range of Hi values of 10-25 kcal mol-1. The upper yield curves correspond to Hi ) 25 kcal mol-1 for both surrogate products, while the lower curves correspond to Hi ) 10 kcal mol-1. At the reference temperature of 35 °C Ki values are known, so the assumed value for Hi has no effect on calculated results. At lower temperatures, however, the assumed Hi values will determine how much variation in yield occurs. As temperature decreases, the predicted yield increases due to an increase in partitioning coefficient values. For the assumed range of Hi, a 10 °C decrease in temperature results 2132

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FIGURE 3. Daily concentration profiles for HYA, OH, and S1+S2 in base case simulation. HYA and OH are gas-phase concentrations. S1+S2 is the sum of gas- and aerosol-phase concentrations for both compounds. HYA values shown have been multiplied by 0.4. in an increase in yield for HYA by approximately 20-60% at Mo ) 50 µg m-3 and 50-150% at Mo ) 5 µg m-3. As the temperature deviates further from the reference temperature, both the change in yield and the uncertainty in the yield due to Hi uncertainty increase. Comparison of Figures 1 and 2 shows how the different Ri and Ki values for HYA and AP result in somewhat different shaped yield curves and temperature behavior. Specifically, for the range of Mo and temperature shown, AP aerosols exhibit a greater sensitivity to temperature, despite identical Hi values for both AP and HYA products. Thus, on the basis of SOA yield, temperature is predicted to have a significant effect. Experimental results for m-xylene support this prediction, showing higher aerosol yield at 27 °C than at 35-40 °C (7). Yield calculations and measurements, however, use idealized systems that are not representative of a dynamic atmosphere. To account for the effects of diurnal temperature and gas-phase chemistry cycles, box model simulations were run to investigate the influence of temperature on ambient SOA concentrations. Base Case Simulation. The HYA base case simulation described in Table 2 was run using three different temperature profiles (see Figure S1, Supporting Information). The base case profile varies temperature over a range of 25 ( 10 °C with a maximum occurring at 3:00 PM and a minimum at 4:00 AM. A second profile has a smaller variation of 25 ( 5 °C, while the third profile held temperature constant at 25 °C. The concentrations of the aerosol precursor HYA, OH radical, and the semivolatile products S1 and S2 were the same in all simulations. Figure 3 shows these concentrations for days 9 and 10 of the simulation, when a repeating constant daily cycle has been established. OH radical concentrations were fixed with a maximum at 12:00 noon and falling to zero during the night. HYA is emitted at a constant rate and builds up at night when no oxidation reaction occurs. Once OH radical concentrations rise during the day HYA reacts and its concentration drops. As a result, HYA concentrations peak at approximately 7:00 AM and reach a minimum near 4:00 PM. The semivolatile products S1 and S2 follow the reverse cycle, peaking in the afternoon and reaching a minimum in the morning. The curve shown in Figure 3 represents the total amount of semivolatile product and is the sum of gasand aerosol-phase concentrations of both S1 and S2. Consequently, when considering SOA partitioning there are two relevant cycles. The temperature cycle controls partitioning coefficient values, while the total semivolatiles cycle determines how much material is available to partition. In these simulations, both cycles have a maximum at 3:00-

FIGURE 4. Daily HYA organic aerosol concentrations for three temperature profiles, T ) 25 ( 0, ( 5, ( 10 °C.

FIGURE 5. Daily HYA organic aerosol concentrations for Hi ) 0, 10, 17.5, 25 kcal mol-1.

4:00 PM, while their minimums are at 4:00 AM and 7:00 AM, respectively. It should be emphasized that the timing of these cycles is specific to this simplified model. In the real atmosphere temperature cycles may be different and would be expected to vary considerably from day to day. More importantly, precursor emission rates are not constant in a real atmosphere and will vary throughout the day due to vehicle use, manufacturing schedules, temperature and sunlight driven vegetative emissions, and other factors. As a result, the timing of peaks in semivolatile concentration is 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 serve as a general model to assess the importance of temperature variations relative to fluctuations due to varying semivolatile concentrations.

a temperature-dependent description of organic aerosol partitioning. Vaporization Enthalpy. The Hi value of 17.5 kcal mol-1 used for the base case simulation is based on very preliminary estimates, and true Hi values may be expected to vary within the range of 10-25 kcal mol-1. To explore the sensitivity of model results to assumed values of vaporization enthalpy a set of simulations was run using different Hi values. Figure 5 shows calculated SOA concentrations for Hi ) 0, 10, 17.5, and 25 kcal mol-1. The intersection of the curves in mid-afternoon occurs because at this time of day the temperature is equal to T*, the reference temperature (35 °C). At T* partitioning coefficients are specified and Hi values have no effect. For a different temperature profile or different T* the timing and existence of this intersection point would vary. The lower curve in Figure 5 shows the calculated SOA concentration for Hi ) 0. The shape of the profile is similar to the constant temperature simulation in Figure 4 with variations due solely to changes in semivolatile concentration. Even though temperature is not constant, it has no effect when Hi ) 0. As can be seen from the other three curves, as Hi increases, sensitivity to temperature increases and SOA concentrations rise during the cooler nighttime hours. Results for AP (Figure S4) show an even greater sensitivity to temperature. Even for the lowest expected Hi (10 kcal mol-1), aerosol concentration increases are significant, with SOA levels higher by up to 36% at night. At the upper range of expected Hi, nighttime concentrations are almost twice what would be predicted if temperature effects were not taken into account. One implication is that constant temperature partitioning coefficients are likely inadequate for describing SOA concentrations in the temperature-varying conditions of the atmosphere. Prediction errors will be greatest when partitioning coefficients have been measured at a temperature outside or near the limits of the actual temperature range. The wide difference in predicted concentration between the 10 and 25 kcal mol-1 curves is an estimate of the range of potential temperature effects. When Hi is small the effect of temperature is less significant with a low-temperature peak only slightly evident at night. Conversely, when Hi is large temperature effects are much more important and the low-temperature peak is even larger than the original daytime peak. Clearly, a more certain determination of temperature dependence is needed to accurately predict SOA partitioning behavior. Precursor Emissions. Estimates of aerosol precursor emissions, particularly for biogenic compounds, are highly uncertain. To assess the impact of emission levels on simulated temperature effects simulations were run using different

Predicted organic aerosol concentrations for the base case simulation using each of the three temperature profiles are shown in Figure 4. For the constant temperature profile SOA mass mirrors semivolatile concentrations and peaks in midafternoon and has a minimum shortly after sunrise. The variation in SOA mass in this simulation is entirely due to changes in semivolatile concentrations since temperature is held constant. It provides a background variation against which temperature-derived fluctuations can be compared. For the variable temperature profiles the afternoon peak is suppressed due to high temperatures, while SOA mass increases in the early morning coincident with the minimum in each temperature cycle. The total amount of semivolatile material at a given time of day is the same for each simulation. When temperature is higher mass shifts to the gas phase, while at lower temperatures mass shifts into the aerosol phase. For the ( 10 °C temperature profile, HYA SOA concentrations decrease by 16% at high daytime temperatures, while they increase 22% at cooler nighttime temperatures, creating a second peak during the middle of the night. Results for AP (Figure S3) show a larger temperature effect with a 24% decrease during the day and a 34% increase at night. Similar behavior, but with somewhat different timing, has been observed for wintertime SOA concentrations in California’s San Joaquin Valley (29) where SOA concentrations peaked in late afternoon to early evening. Variations due to temperature are similar in magnitude to those produced by changes in semivolatile levels. Results suggest that temperature has a significant influence on both the magnitude and timing of peak SOA levels. Consequently, it is unlikely that the currently available constant temperature partitioning coefficients determined at high temperatures can adequately represent atmospheric SOA partitioning behavior. Accurate air quality model predictions will require

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FIGURE 6. Daily HYA organic aerosol concentrations for HYA emissions ) 0.5, 1.0, 2.0 times base case simulation levels.

FIGURE 7. Daily HYA organic aerosol concentrations for POA ) 0, 10, 20 µg m-3.

precursor emission rates. For these runs, all input parameters were the same as in the base case simulation, except that aerosol precursor emissions were increased and decreased by a factor of 2. By changing precursor emissions, the amount of semivolatile product available to partition between the gas and aerosol phases is changed proportionately. Figure 6 depicts the effect of these changes on SOA concentrations. The most obvious result of changing the emission rate is an even greater change in the amount of SOA. This behavior is due to the nonlinear nature of absorptive partitioning. Adding semivolatile material to the system initially causes SOA levels to increase proportionately, but as this additional absorbing organic mass is formed, the yield also increases so that a greater fraction of semivolatiles will partition to the aerosol phase. Consequently, doubling precursor emissions in this scenario results in a 3-fold increase in SOA concentration. More dramatically, reducing emissions by half causes virtually all of the remaining semivolatile material to shift to the gas-phase. It should be noted that in these simulations there are no other sources of absorbing organic matter. The effect of primary organic emissions will be discussed further in the next section. While the total amount of SOA changes quite dramatically in these simulations, the effect of temperature is similar. For the low emission case, aerosol concentrations are negligible for most hours of the day, only rising above zero at night when temperatures are the coolest. For the base case and doubled emission simulations the timing of maximum and minimum concentrations and the ratio of these concentrations are similar such that the shape of the SOA concentration profiles is nearly the same. Thus, precursor emission rates seem to have a greater influence on the overall amount of SOA and the absolute magnitude of temperature effects than on the relative effects of temperature. Primary Organic Aerosol. In the atmosphere primary organic aerosol (POA) emissions will also be present so that a background absorbing media is always available. We performed simulations using three different POA emission rates. These organic emissions were assumed nonvolatile, resulting in a constant concentration of primary absorbing organic matter for each simulation. POA emission rates were specified so as to produce primary absorbing organic concentrations of 0, 10, and 20 µg m-3. Figure 7 shows the results of these simulations. For each case, the addition of primary organic aerosol shifts organic aerosol concentrations higher, with concentration variations reduced slightly at higher POA levels. This damping behavior is not the major effect as the difference between maximum and minimum concentrations remains approximately the same for all POA levels. This is different from results seen for precursor emissions (Figure 6) where the magnitude of

variations due to temperature is proportional to average concentration. It is important to note that the difference in concentration between runs is greater than the difference in POA. Compared to the base case (POA ) 0) adding 10 µg m-3 of POA causes total organic aerosol to increase by approximately 15 µg m-3, while adding an additional 10 µg m-3 of POA results in total organic aerosol further increasing by 13 µg m-3. As mentioned previously, increasing the amount of absorbing organic matter increases the aerosol yield. Consequently, SOA concentrations will be higher when POA is present. POA, however, seems to have only a small damping effect on the magnitude of temperature influenced variations in concentration.

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Supporting Information Available Additional figures of R-pinene simulation results. This material is available free of charge via the Internet at http:// pubs.acs.org.

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Received for review August 2, 2000. Revised manuscript received March 2, 2001. Accepted March 8, 2001. ES001547G

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