Environ. Sci. Technol. 2003, 37, 3662-3670
Secondary Organic Aerosol Formation from Aromatic Precursors. 1. Mechanisms for Individual Hydrocarbons WIPAWEE DECHAPANYA,† ALEXANDRA EUSEBI, YOSUKE KIMURA, AND DAVID T. ALLEN* Center for Energy and Environmental Resources, University of Texas, 10100 Burnet Road, Mail Code R7100, Austin, Texas 78758
Quantitative kinetic and physical phase partitioning models of secondary organic aerosol (SOA) formation resulting from the reactions of aromatic species were integrated into a mechanism for gas-phase reactions. Using the resulting model, analyses of the sensitivity of SOA formation to several parameters (e.g., VOC/NOx ratio, rate parameters) were performed. Results indicated that aerosol yield (SOA formed per amount of hydrocarbons reacted) depends on the extent of conversion of parent hydrocarbons, partitioning coefficient, initial aerosol mass concentration, and rate parameters. On the basis of the sensitivity studies, models for SOA yield were developed for 11 aromatic compounds. Comparison of the results from current SOA models to the results from this study suggests that mechanisms describing SOA formation from aromatic species must incorporate the reactions of reactive intermediates.
produced by the reactions of compound i, and [VOCi]o is the amount of compound i emitted. Alternatively, the formation of SOA is sometimes modeled using the selectivity of VOC precursor oxidation reactions to SOA (1):
d[VOC] d[SOA] d[PMk] ) ) Rk ) RkkOH[OH][VOC] (2) dt dt dt where Rk is the selectivity of the reacted volatile organic compound to secondary organic aerosol species k (PMk). The fractional aerosol coefficient (FAC) and the selectivity to SOA (Rk) are generally assumed to be constant. Odum et al. (3, 4), however, have presented smog chamber data that indicate that SOA yield and selectivity are not constant but rather increase with the extent of reaction of the organic precursor. These results were analyzed using a physical model. In this model, an SOA precursor is assumed to react through multiple pathways, producing a group of compounds that can partition between gas and condensed phases. A partitioning coefficient (Kom,i) is defined for each of the oxidation products i of the SOA precursor (5, 6):
Kom,i ) Fi,om/AiMo
where Ai is the gas-phase concentration of compound i, Fi,om is the concentration of compound i in the condensed organic material (om) phase, and Mo is the organic aerosol mass concentration. The total yield of SOA for the precursor is then the sum of all of the oxidation products that partition into the condensed phase. Odum et al. (3) show that this can be expressed as
Y)
Secondary organic aerosol (SOA) is formed in the atmosphere when the oxidation of gas-phase organic compounds leads to the formation of low-volatility reaction products, which partition into the condensed phase. Because of differences in reactivity and differences in the volatility of reaction products, organic compounds emitted to the atmosphere can have very different SOA yields. For example, relatively high molecular weight compounds that react rapidly in the atmosphere, such as sesquiterpenes, have high SOA yields. In contrast, low molecular weight organics that are relatively unreactive (alkanes such as heptane, octane, and nonane) have very low SOA yields. To account for these differences in reactivity and volatility of reaction products, fractional aerosol coefficients (FACs) have been used in many regional air quality models (1). FACs (2) typically assign a SOA yield to the emissions of individual organic compounds:
FACi ) [SOAi]/[VOCi]o
(1)
where FACi is the fractional aerosol coefficient for compound i, [SOAi] is the concentration of secondary organic aerosol * Corresponding author e-mail:
[email protected]; telephone: (512)471-0049; fax: (512)471-1720. † Present address: Department of Chemical Engineering, Ubonratchathani University, Ubonratchathani 34190, Thailand. 3662
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(
RiKom,i
∑Y ) M ∑ 1 + K i
o
i
Introduction
(3)
i
om,iMo
)
(4)
where Ri is the fraction of the reacted VOC that forms product i. Smog chamber data, which show that SOA yield increases with extent of reaction, are replicated very well by this physical model. As more SOA is produced, the greater mass of the condensed phase leads to more reaction product partitioning into the condensed phase. This model is appropriate for SOA formation processes that are parallel reactions and that lead to aerosol phase product in one step:
VOCi f RkPMk + other products However, not all SOA precursors lead to SOA in a single reaction step. Aromatic compounds, which are believed to contribute substantially to SOA in urban areas (7), have the potential to form aerosol products through a two-step reaction (8-10):
VOCi + OH• f ajAPRj + other products APRj + OH• f akPMk + other products where OH• is the hydroxyl radical, APRj is an intermediate aerosol precursor, aj and ak are the stoichiometric coefficients for the aerosol precursor and condensable products PMk, respectively. The full chemistry of aerosol formation from aromatic precursors is not yet known, in large part because most studies 10.1021/es0209058 CCC: $25.00
2003 American Chemical Society Published on Web 07/16/2003
of aromatic reactions under atmospheric conditions have identified only 30-50% of the mass of reaction products (11, 12). Nevertheless, recent studies and reviews [for example, Forstner et al. (8), Jang and Kamens (9), and Atkinson (12)] have generally grouped the products of aromatic reactions into ring-retaining aromatic products, ring-retaining nonaromatic products, and ring-opening products and have found that nonaromatic products (both ring-opening and ring-retaining) predominate. In the case of both ring-opening and ring-retaining nonaromatic products, the initial products of reaction with hydroxyl radical contain one or more double bonds and, therefore, remain reactive. Thus, aromatic hydrocarbons have the potential to form SOA through the two-step process outlined above rather than through a single step. As examples of these processes, the reactions for 1,3,5trimethylbenzene and toluene will be described in more detail. The oxidation of 1,3,5-trimethylbenzene (135-TMB) proceeds primarily through reaction with the hydroxyl radical (11, 13). The OH radical either abstracts hydrogen from 135TMB or adds to the aromatic ring (14). Perry et al. (15) have suggested that only 2.0-3.5% of the 135-TMB that reacts proceeds through the hydrogen abstraction route. The structures of the all of the reaction products due to hydroxyl addition to the aromatic ring are not yet known, but representative structures, such as the dicarbonyls shown in Figure 1, can be assumed. The dicarbonyls are the result of ring cleavage and are denoted as aerosol precursors, APT1 for aerosol precursor from 135-TMB, in Figure 1. APT1 further reacts with OH radical and with ozone if ozone is present at sufficient concentration to form products that can partition into the particulate phase, TPM1 and TPM2, as shown in Figure 1c. Thus, the overall mechanism is a series/parallel network:
that the intermediates will undergo, it is clear that series/ parallel networks can be important in modeling SOA from aromatic precursors. To address the uncertainty in the exact pathways, the sensitivity of SOA formation to changes in the reaction rates of reactive intermediates and the values of partitioning coefficients will be examined using a box model. Predictions of SOA formation through series/parallel reaction networks will be developed for 11 commonly emitted volatile aromatic compounds, and these predictions will be compared to predictions from a simpler, one-step, SOA formation process.
135-TMB + OH• f aAAPT1 + other products
135-TMB + HO• ) 0.18HO2• + 0.804RO2R• + 0.01RO2N + 0.621MGLY + 0.18CRES + 0.03BALD + 0.569DCB1 + 0.097DCB2 + 0.114DCB3 + 2.273XC
APT1 + OH• f aBTPM1 + aCTPM2 + other products Because aerosol formation for aromatics can be the result of a set of series/parallel reactions of hydrocarbon precursors and aerosol precursors with OH radicals, the yield of SOA will be a strong function of the extent of reaction. At low conversion, before significant concentrations of the reactive intermediate species have built up, yields of SOA will be relatively low. As the extent of reaction increases, the concentrations of the reactive intermediates build and the yield of SOA increases. This increasing yield of SOA with increasing extent of reaction is the same phenomenon predicted by the model of Odum et al. (3) based on physical partitioning processes. For toluene, the situation is similar. Jang and Kamens (9) report a large number of ring-opening products, ringretaining nonaromatic products, and ring-retaining aromatic products for the oxidation of toluene in the presence of propene and nitrogen oxides. Although yields are not directly reported, IR spectra of the SOA generated in the chamber do not show a strong aromatic band, indicating relatively low yields of aromatic ring-retaining products, consistent with the results reported by Holes et al. (16) for trimethylbenzene. Both classes of nonaromatic products (ring-retaining and ring-opening) contain large numbers of species that remain reactive, suggesting again that a series/parallel network of reaction will be a major route to SOA formation. The remainder of this paper will describe methods for quantifying SOA yield for aromatic species accounting for the series/parallel nature of the chemical reactions and coupling the chemical kinetics with a physical model of partitioning. While there is still considerable uncertainty about the exact structure of the reactive ring-opening and non-ring-opening nonaromatic products and the reactions
Methodology Modeling SOA Formation in Trimethylbenzene Photooxidation. The reactions of trimethylbenzene will be used to illustrate the methods that were used to quantify SOA yields of aromatic hydrocarbons through series/parallel networks. The mechanisms, stoichiometric coefficients, and rate parameters will be drawn both from the literature and from previous analyses in our laboratory (14, 16). Data on SOA yields will be drawn from the work of Odum et al. (3). The mechanisms for SOA formation will be integrated into a comprehensive model of gas-phase atmospheric chemistry. The model of gas-phase chemistry that will be used in this work, in box model mode, has been developed by the Statewide Air Pollution Research Center (SAPRC) of the University of California. SAPRC has developed software, SAPRC 99, to model gas-phase chemistry of mixtures of more than 300 common air pollutants (17). To convert the 135-TMB reaction in SAPRC, which only accounts for the initial reaction of 135-TMB, into a form that accounts for SOA, a specific format is required. The form of the reaction that is currently in SAPRC is
This single SAPRC reaction accounts for addition reactions, abstraction reactions, and ring cleavage reactions as described below. On the basis of the distribution suggested by Perry et al. (15), 3% of products result from initiation by hydrogen abstraction, and 97% result from OH radical addition. The hydrogen abstraction route of 135-TMB results in a dimethylbenzaldehydye (in SAPRC notation), along with an NO to NO2 conversion and the production of HO2 radical (rxn 1). Since the product from the hydrogen abstraction of 135TMB has a structure similar to benzaldehyde (BALD) but with two extra carbon atoms and because every product cannot be included in the SAPRC mechanism, the dimethylbenzaldehyde is represented by BALD + 2XC. The abstraction route is represented as
3%: 135-TMB + HO• ) BALD + 2XC + RO2R (rxn 1) where RO2R is an operator representing peroxy radical reactions that result in NO to NO2 conversion and formation of an HO2 radical. This route is assumed to account for 3% of the reacted 135-TMB (17). Addition of the OH radical to trimethylbenzene occurs at any of the three unsubstituted aromatic carbons. All are equivalent. Initial reaction with OH radical will produce a trimethyl-phenol (cresol with two extra carbon atoms, CRES +2XC) and HO2 radical. This pathway accounts for 18% of the reacted trimethylbenzene (17):
18%: 135-TMB + HO• ) CRES + 2XC + HO2• (rxn 2) In the original SAPRC mechanism, the remainder of the OH VOL. 37, NO. 16, 2003 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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FIGURE 1. (a) Hydrogen abstraction from 1,3,5-trimethylbenzene by OH radical. (b) Reaction pathway for OH addition to 1,3,5-trimethylbenzene. (c) Reaction pathways for aerosol precursors. 3664
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adduct (79% of total reaction) further reacts with O2 with conversion of NO to NO2 to form uncharacterized reactive aromatic ring-fragmentation products (DCB1, DCB2, and DCB3), methylglyoxal (MGLY), and peroxy radical:
79%: 135-TMB + HO• ) 0.72DCB1 + 0.12DCB2 + 0.15DCB3 + 0.786MGLY + 0.979RO2R + 2.345XC + 0.012RO2N (rxn 3) Performing a weighted summation of rxns 1-3 leads to the overall gas-phase reaction currently in SAPRC. In the modified mechanism accounting for aerosol formation, rxn 3 is replaced by rxn 4, which represents OH radical addition, further reaction with O2, and conversion of NO to NO2 to generate methylglyoxal (MGLY), peroxy radical, and unsaturated dicarbonyls, such as 2-methyl-4-oxopent-2-enal, referred to as aerosol precursor from trimethylbenzene (APT1). This pathway is still assumed to account for 79% of all reactions (18):
79%: 135-TMB + HO• ) MGLY + APT1 + RO2R (rxn 4) Combining the addition, abstraction, and ring-fragmentation reactions, the modified overall stoichiometry for the reaction of 135-TMB is
net: 135-TMB + HO• ) 0.03BALD + 0.18CRES + 0.81RO2R + 0.18HO2• + 0.79MGLY + 0.79APT1 + 0.42XC (rxn 5) The species identified as BALD (pathway 1 in Figure 1); CRES (pathway 2 in Figure 1a); MGLY (pathway 3 in Figure 1b); TPM1, TPM2, and APT1 (pathway 4 in Figure 1c) represent chemical species. The identifications RO2R and RO2N represent operators, which indicate product formation pathways. More detailed descriptions of SAPRC variables are provided in the Nomenclature section and by Carter (17). APT1 further reacts with OH radicals via addition at the carbon double bond to form two types of condensable products, referred to as TPM1 and TPM2. TPM1 is the aerosol product containing an organonitrate group (as shown in pathway 4a, Figure 1c), and TPM2 (pathway 4b, Figure 1c) is the product without the organonitrate group. TPM1 is produced through OH radical addition to the double bond in APT1. The OH adduct reacts with O2 and NO to form organonitrate. Experimental results from Eusebi (14) and Holes et al. (16) suggest that 25% of APT1-OH addition reactions produce TPM1. The remaining APT1-OH adduct reacts with O2 and NO to form TPM2, NO2, and HO2 radicals:
APT1 + HO• ) 0.25TPM1 + 0.75TPM2 + 0.25RO2N + 0.75RO2R (rxn 6) Reactions 5 and 6 describe a condensed mechanism of SOA formation for 135-TMB, which proceeds through two steps: gas-phase reaction of 135-TMB with OH radicals to produce APT1 and reaction of APT1 with OH radical to form two types of potential aerosol products (TPM1 and TPM2). Stoichiometric coefficients in rxns 1-6 are obtained from the evaluation of atmospheric chamber experiments. Table 1 summarizes these coefficients and cites the source of the data. There are a number of approximations and simplifications associated with the reaction network for trimethylbenzene outlined above. One simplification is the assumption that the aerosol precursor species reacts exclusively with hydroxyl radical. Ozone reactions with the dicarbonyl species and photolysis of the dicarbonyl species are also possible. Under polluted daytime urban conditions, with hydroxyl radical concentrations of 107 molecules per cm3and ozone concen-
TABLE 1. Stoichiometric Parameters of 1,3,5-Trimethybenzene Reactions with OH Radicals Forming Aerosol Products and Their Sources notation CRES BALD MGLY HO2. RO2R
description cresol benzaldehyde methylglyoxal hydroperoxy radicals operator RO2R
stoichiometric coeff (SC)
0.18 0.03 0.79 0.18 0.81 (rxn 5) 0.75 (rxn 6) APT1 aerosol precursor 0.79 RO2N operator RO2N 0.25 TPM1 aerosol product species 1 0.25 TPM2 aerosol product species 2 0.75
sources of SC SAPRC-99 SAPRC-99 ref 18 SAPRC-99 ref 18, SAPRC-99 ref 16 ref 18 ref 16 ref 16 ref 16
trations of 100 ppbv, the rate of ozone reaction with dicarbonyl species would typically be 1-3 orders of magnitude slower than the reaction of the dicarbonyl with hydroxyl radical, depending on the structure of the dicarbonyl species. At the upper end of this range, reactions with ozone will be significant. Ozone reactions may also be significant at night and at other times with high ratios of ozone to hydroxyl radical concentrations. Photolysis reactions can also be a major sink for dicarbonyl species in the atmosphere, depending on the structure of the dicarbonyl. Bierbach et al. (19) report photolysis rates for a number of dicarbonyls when illuminated by fluorescent lamps (320-480 nm). For selected dicarbonyls, such as 3-hexene-2,5-dione, photolysis may be a more important sink than reaction with hydroxyl radical, but for other species, such as butenedials, photolysis is less significant. This is clearly an area that needs further investigation, but for the modeling presented in this work, the available information on photolysis is too limited to incorporate systematically. Thus, the mechanisms and modeling results presented in this work are intended to be representative rather than definitive. Another assumption made in the mechanism development is that the replacement of existing SAPRC species, such as the uncharacterized reactive aromatic ring-fragmentation products (DCB1, DCB2, and DCB3) with aerosol precursor species will not have a significant impact on the accuracy of the SAPRC predictions of reactive intermediate concentrations (e.g., hydroxyl radical and ozone concentrations). Comparisons of simulations with and without the aerosol formation pathways showed small differences in ozone and hydroxyl radical concentrations, and these changes lead to much less uncertainty than the uncertainties in the mechanisms outlined above. Nevertheless, it should be recognized that as combined gas- and aerosol-phase chemistry models evolve, comprehensive reparametrizations of the models will be required. As described above, formation of secondary organic aerosol proceeds in part through a series/parallel network. To analyze these series/parallel networks quantitatively, all reaction rates must be determined. Some of the reactions are well-studied (such as trimethybenzene + OH radical) while for others the exact structure of the chemical species involved is not completely certain, and rate constants must be estimated (e.g., APT1 + OH radical). The reaction rate constants for the reaction of aerosol precursors, such as APT1 with OH radical, were estimated by assuming that dicarbonyl species were the primary aerosol precursors and the rates of reaction of these precursors with hydroxyl radical were calculated by using the Atmospheric Oxidation Program. The Atmospheric Oxidation Program for Microsoft Windows 3.1 (AOPWIN) estimates the rate constant for atmospheric gasphase reactions between photochemically produced hydroxyl radicals and organic chemicals (20). The estimation methods used by AOP are based on the structure-activity relationship VOL. 37, NO. 16, 2003 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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TABLE 2. Kinetic Parameters Used in Modeling and Estimates of SOA Formation from Reaction of Hydrocarbon Precursors parameters
description
value (cm3/molecule-s)
source
k135-TMB kAPT1
overall rate constant of 135-TMB with OH radical rate constant for addition of OH radical to double bond of dicarbonyl species APT1 (aerosol precursor from 135-TMB)
5.75E-11 2.60E-11
ref 17 AOPWIN program
TABLE 3. Model Parameters Used in Estimates of SOA Formation parameters
descriptions
calculation procedures
Kom,i VPi ∆Mo
partitioning coefficients of compound i vapor pressure of product i mass of semivolatile products partitioning into aerosol phase
estimated from chamber data (3, 4) Antoine method and modified grain method calculated in simulation; various concentrations of seed aerosol (Mint) assumed
(SAR) method developed by Atkinson (21, 22), Atkinson and Aschmann (23, 24), Atkinson et al. (25), Biermann et al. (26), Kwok and Atkinson (27, 28), and Kwok (29). The uncertainty of rate constants predicted from this program, for compounds with a double bond, is typically less than a factor of 2 (30, 31). The kinetic parameters used in the modeling of SOA formation are enumerated in Table 2. Gas/Partitioning Model. In addition to understanding the chemical mechanisms leading to secondary organic aerosol formation, it is also necessary to understand the partitioning of semivolatile secondary organic aerosol (SOA) species between gas and particle phases. This partitioning is described by the equilibrium between the gas-phase concentration of the condensable products and their concentration in the condensed phase. A partitioning coefficient for species i (Kom,i) can be defined in terms of the organic mass concentration as shown in eq 3 (3). This study will use the same partitioning framework. Odum et al. (3) used a model with four parameters (R1, R2, Kom,1, and Kom,2) where R1 and R2 are the yields of condensable products TPM1 and TPM2 (assumed constant), and Kom,1 and Kom,2 are the partitioning parameters for these species. In this work, instead of assuming these parameters are all adjustable, the yields of TPM1 and TPM2 will be obtained from the chemical mechanism described in the previous section. The partitioning parameters for the two products (Kom,1 and Kom,2) will be assumed to have the same ratio as the vapor pressure of the presumed aerosol products (i.e., Kom,1/Kom,2 ) vapor pressure of other products/vapor pressure of organonitrate products. The vapor pressures of the condensable products from the individual hydrocarbon precursors were estimated using group contribution methods, based on the Antoine equation (20, 32). The product structures used as input to the group contribution methods are shown in Figure 1. The value of Kom,1 was fit to chamber data, and the value of Kom,2 was estimated using the ratio of vapor pressures at the temperature at which the chamber experiment was performed. Therefore, in this work, only one parameter is adjusted to fit yield data for SOA. A box model has been used to examine the yields of secondary aerosol from individual hydrocarbon precursors. The box model uses the full SAPRC mechanism, including the additional pathways to account for aerosol formation. The simulations were conducted under similar conditions to those performed in experiments reported in the literature (3, 4), and the experimental data were used to determine the one adjustable parameter in the model. Table 3 lists the partitioning parameters, including their descriptions, and the procedures used to estimate them. The estimated partitioning coefficients and vapor pressures of condensable products from the reactions of 124TMB and 135-TMB are reported in Table 4. Note that the temperatures at which the vapor pressures are calculated 3666
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TABLE 4. Estimated Partitioning Coefficients and Vapor Pressures of Condensable Products from Trimethylbenzene Reactions parent hydrocarbon
Kom,1 (m3/µg)
Kom,2 (m3/µg)
VP1 (mmHg)
VP2 temp (mmHg) (K)
1,2,4-trimethybenzene 0.00609 0.000046 0.000113 0.0015 297 1,3,5-trimethybenzene 0.00337 0.00029 0.00026 0.0030 302
TABLE 5. Simulation Conditions for 1,2,4-Trimethylbenzene (3) ROGo (µg/m3)
∆ROG (µg/m3)
Mo (µg/m3)
NOx (ppb)
C3H6 (ppb)
∆HC/NOx (ppb of C/ppb)
Y (%)
2391 3367 1607 1932 1237 1745
1996 2282 1198 1533 1020 1309
113 155 43 78 26.5 53
975 1178 490 590 359 528
300 300 300 300 300 300
4.7 4.3 6.3 6.3 7.7 6.2
5.66 6.79 3.59 5.09 2.60 4.05
correspond to the temperatures of the environmental chamber experiments used to estimate the partitioning parameters. SOA Yields for 124-TMB. Simulation results included the amount of total product TPM1 and TPM2 produced from the hydrocarbons reacted (∆ROG). Six different initial conditions (3) were employed to simulate SOA formation and to estimate a single adjustable parameter for the partitioning of lowvolatile products for 124-TMB. These conditions are shown in Table 5. The simulations were performed at 24 °C, representing average temperature from Odum et al. (3) data, which ranges from 22 to 26 °C. SOA yields for 124-TMB from the simulations were compared to those from Odum’s chamber experiments (Note that slightly different mechanisms, rate parameters, and partitioning coefficients were used for 124-TMB as compared to 135-TMB. The mechanisms and parameters for all of the species used in this work are given in the Supporting Information.) Figure 2 illustrates SOA yields from the simulations, using the model developed in this work [fit to the Odum et al. (3) data with one adjustable parameter, Kom,1]. Also shown are the observed SOA yields from the chamber data and the yield as predicted by the four-parameter model originally reported by Odum et al. (3) to describe the data. The simulations reported in this work using the one-adjustable parameter series/parallel reaction model are not quite as accurate as the four-parameter model empirically fit to the data. However, the model is in good qualitative agreement with the data (for additional comparisons, see ref 33) and is suitable for sensitivity studies aimed at understanding the role of series/ parallel reaction networks.
FIGURE 2. SOA yields for 1,2,4-trimethybenzene, NOx ) 359-1178 ppb, C3H6 ) 300 ppb, and ∆HC/NOx ) 4.3-7.7 ppb of C/ppb.
TABLE 6. Initial Conditions for Box Model Simulation for 1,3,5-Trimethybenzene in the Presence of Houston Air Pollutants simulation time (h) NO (ppm) NO2 (ppm) CH4 (ppm) NMOC (ppm of C) CO (ppm) temp (K)a
8 0.20806 0.01095 2.5606 6.8367 2.1457 312
a Temperature is a function of time. The value reported is at the beginning of the simulation. Temperature is based on ambient condition plus 10 deg to account for heating inside chamber.
Results and Discussion Sensitivity Analysis: Case Study for 135-TMB. The model developed in this work, based on a chemical reaction mechanism and a single partitioning parameter, showed reasonable agreement with Odum’s chamber experiments for 124-TMB. The model was therefore used to investigate the sensitivity of SOA formation via a series/parallel network to parameters such as VOC/NOx ratio, reaction rate parameters, and partitioning parameters. The results of sensitivity analyses for 135-TMB are presented. 1,3,5-TMB was selected for initial examination because of the availability of smog chamber experiments that provided estimates for the branching ratio for organonitrate formation (and hence accurate values of the stoichiometric coefficients, ak) (14, 16). The box model used in the simulations used the full SAPRC mechanism modified to account for aerosol formation from 135TMB. Initial conditions and composition of hydrocarbons used in the box model simulations are similar to those seen in the Houston area (34). Table 6 lists these conditions. The composition of non-methane organic carbon (NMOC) is reported in the Supporting Information. SOA Yield as a Function of Conversion and VOC/NOx. Shown in Figure 3a,b are the yields of aerosol products as a function of 135-TMB conversion and time, for initial VOC/ NOx ranging from 4 to 31 (31 is the ratio for the base case) ppm of C/ppm. Note that 135-TMB is only one component of the VOC mixture in the simulation (see Supporting Information). The analysis was performed for a closed system. While the concentration of NMOC was fixed in all simulations, concentrations of NO2 and NO were changed to vary the VOC/NOx ratio between 4 and 31. In revising the NOx concentration, the initial ratio of NO to NO2 was kept constant. Figure 3a suggests that SOA yield (Y), which is the ratio of the amount of condensable products partitioning to
FIGURE 3. SOA yields for 1,3,5-trimethybenzene reported as a function of conversion and a function of time of day (reaction time). particulate phase to the amount of primary hydrocarbon reacted (∆Mo/∆ROG), depends strongly on 135-TMB conversion. The yield is a parabolic function of conversion. SOA yields at various VOC/NOx ratios as a function of conversion collapse onto a single line. In contrast, Figure 3b shows that SOA yield, as a function of reaction time, depends on the VOC/NOx ratio. The reason for the differences between Figure 3, panels a and b, is the change in hydroxyl radical concentration caused by changing VOC/NOx ratio. As VOC/ NOx ratio changes hydroxyl radical concentrations, the rate of the SOA formation reactions change and therefore the dependence of SOA yield on reaction time changes. In contrast, when SOA yield is expressed as a function of conversion, the changes in hydroxyl radical concentrations are accounted for in the conversion, and the yields at different VOC/NOx concentrations therefore collapse onto a single line. This has implications in formulating SOA yield parameters for photochemical grid models, as will be described later in this paper. SOA Yields as a Function of Base Hydrocarbon Composition. To examine the effect of the base hydrocarbon composition on SOA yield, the concentration of alkenes was reduced by 50% from the base case, and the reduced mass was replaced as alkanes. Then, the box model simulation was performed again, and the SOA yield for 135-TMB was compared to the SOA yield for the original base hydrocarbon mixture. The calculation was also repeated with 50% of the aromatics in the base mixture converted to alkanes. The results, shown in Figure 4, suggest that SOA yield, expressed as a function of conversion, is relatively independent of base VOL. 37, NO. 16, 2003 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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FIGURE 6. SOA yields for 1,3,5-TMB for initial particulate mass (Mint) equal to 5 and 15 µg/m3.
FIGURE 4. SOA yields for 1,3,5-trimethybenzene using different base hydrocarbon compositions, reported as a function of conversion.
production of organic particulate products is barely detectable at most levels of conversion. The behavior shown in Figure 5 is due to the series/parallel nature of the aerosol formation chemistry. Hydrocarbon precursor reacts with OH radical to form aerosol precursor (APT), and then APT further reacts with OH radical to form semivolatile product species TPM: kTMB
kAPT
TMB 98 APT 98 TPM
FIGURE 5. SOA yields for 1,3,5-trimethybenzene as a function of conversion, for a range of values of the rate constant of aerosol precursor reaction with hydroxyl radical. hydrocarbon composition. As with the sensitivity calculations done varying the VOC/NOx ratio, reporting SOA yield as a function of conversion leads to predictions that are independent of base hydrocarbon composition because changes in hydroxyl radical concentration induced by changes in base hydrocarbon composition are accounted for in the conversion. SOA Yields as a Function of Rate Parameters. The sensitivity of SOA yield to the values of rate parameters was also examined. Since the precise structure of aerosol precursors is uncertain, the rate constant for the reaction of aerosol precursor from 135-TMB (kAPT1) with hydroxyl radical was varied from 1.00E-9 to 1.00E-13 cm3/molecule-s. SOA yields are reported as a function of 135-TMB conversion in Figure 5. As is evident from Figure 5, SOA yield is rate parameter dependent. Increasing the rate constant of the reaction of aerosol precursor with OH• enhances the SOA yield. However, ultimately SOA yields converge at the same point for 100% conversion. At kAPT1 less than 10-12 cm3/molecule-s, the 3668
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As the value of kAPT is reduced, APT accumulates as trimethylbenzene reacts rather than forming condensable species (TPM). This strong dependence of the SOA yield on the hydroxyl radical rate constant of the aerosol precursor coupled with the reasonable agreement of the base case SOA yield as a function of conversion between the model and chamber studies suggests that the rates used in the model (based on dicarbonyl reactivities) are reasonable estimates. SOA Yields as a Function of Organic Particulate Mass. The dependence of gas/particle partitioning on the organic particulate mass was examined for 135-TMB. The initial amount of aerosol mass that was used in the box model (Mint) was varied from 5 to 15 µg/m3. SOA yields for both cases are compared in Figure 6. The SOA yields for Mint of 5 and 15 µg/m3 are different by a factor of 2 for the buildup period of aerosol precursor concentrations (up to 40% conversion). Beyond 40% conversion, the differences gradually increase and then reach a constant factor of 3 after 50% conversion. This dependence of aerosol yield on Mint is expected based on the gas/particle partitioning model used. SOA Yields as a Function of Partitioning Coefficient (Kom). Partitioning coefficients (Kom,1 and Kom,2) are employed to describe the physical partitioning of semivolatile products into the condensed organic phase. To examine how SOA yield is sensitive to the values of these parameters, Kom,1 was varied by factors of 2 and 3 from the original value. Because Kom,1 and Kom,2 are related through the vapor pressures of the compounds, changing Kom,1 will result in proportional changes in Kom,2. SOA yields represented as a function of 135-TMB conversion at the base case, double the base case, and triple the base case Kom values are shown in Figure 7. At 60% conversion the yields at triple and double Kom are approximately 2.3 and 1.6 times higher than yield at the original Kom, indicating that the effect of Kom is close to linear. This is expected based on the gas/particle partitioning model used, and the fact that the SOA formed by the reactions of trimethylbenzene represented a relatively minor addition to the initial aerosol mass.
FIGURE 7. SOA yields for 1,3,5-trimethylbenzene for the original, double, and triple values of Kom,1 and Kom,2. Simulation conditions are based on Carter (34).
duced from dicarbonyl aerosol precursor and not the stoichiometric coefficients of low-volatility products directly from aromatic precursors.) The implications of using fixed yields, as opposed to yields that depend on conversion, are shown in Figure 8. Figure 8a shows the results of simulations for m-xylene. The modified SAPRC model (this work) does not predict significant yields of secondary organic aerosol until conversion of parent hydrocarbon proceeds to approximately 70%. This is because the dicarbonyl aerosol precursors are estimated to react at approximately the same rate as the m-xylene. Sufficient concentration of the dicarbonyl precursors must accumulate for SOA to form. Once the dicarbonyl precursors have accumulated, SOA yield dramatically increases. This is consistent with Odum et al.’s (4) characterization of m-xylene as a low yield aromatic, with SOA yields of only a few percent even at high conversions. Figure 8b shows the results for n-propylbenzene. In this plot, the difference between two models is qualitatively different than the results for m-xylene. For n-propylbenzene, the dicarbonyl aerosol precursors react 2-3 times faster than the n-propylbenzene. There is relatively little accumulation of dicarbonyl precursor, and SOA forms at a lower conversion level of the initial reactant. Taken collectively, the results presented in Figures 3-8 suggest that accounting for the series/parallel nature of SOA formation reactions will be important as models are extrapolated to levels of conversion, both higher and lower, than those examined in environmental chambers. Figure 5 suggests that accurate identification of reactive aerosol precursors and their rate constants will be very important in developing models of SOA formation from aromatic precursors. Figures 3 and 4 suggest that SOA yields, expressed as a function of precursor conversion, will be relatively independent of VOC/NOx ratios and base hydrocarbon composition.
Acknowledgments This material is based upon work supported by the Texas Advanced Technology Program under Grant ATP 0036580156-2001.
Supporting Information Available More detailed information on the conditions used in the box model simulation and mechanisms, rate parameters, and partitioning parameters for all of the aromatic SOA precursors considered in this work. This material is available free of charge via the Internet at http://pubs.acs.org.
Notation
FIGURE 8. Correlations for aerosol yield: ∆Μο vs % conversion of parent hydrocarbons (a) m-xylene and (b) n-propylbenzene. Comparison of SOA Yield Models and Empirical SOA Yield Expressions. In current models of SOA formation, the total yields of low-volatility products [expressed as R1 and R2 in the model of Odum et al. (3, 4)] are assumed to be fixed, while in this work the yields of low volatility products are strong functions of conversion. (Note that the stoichiometric coefficients in this work are assumed constant and represent the stoichiometric coefficients for particulate matter pro-
R1
stoichiometric coefficient for yield of lowvolatility product type 1 from VOC
R2
stoichiometric coefficient for yield of lowvolatility product type 2 from VOC
Rk
selectivity of reacted volatile organic compound to secondary organic aerosol species k
124-TMB
1,2,4-trimethylbenzene
135-TMB
1,3,5-trimethylbenzene
Ai
mass concentration of semivolatile particulate matter species i in gas phase
aA
stoichiometric coefficient for yield of APT1 from 135 TMB
aB
stoichiometric coefficient for yield of TPM1 from APT1
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aC
stoichiometric coefficient for yield of TPM2 from APT1
aj
stoichiometric coefficient for yield of APRj from VOCi
ak
stoichiometric coefficient for yield of PMk from dicarbonyl aerosol precursor (APRj)
APRj
aerosol precursor species j
APTi
aerosol precursor produced from trimethylbenzene species i
AROi
aromatic precursor species i
BALD
benzaldehyde
BACL
biacetyl
CRES
cresol
FACs
fractional aerosol coefficients
Fi,om
concentration of compound i in absorbing organic material (om) phase
GLY
glyoxal •
HO2
peroxy radical
Kom,i
gas/particle partitioning coefficient of species i
Mo or Mtot
total organic aerosol mass concentration
∆Mo or ∆Mi aerosol mass formed from photolysis reaction and partitioning into aerosol phase
XC
extra carbon atom in gas phase
Yi
aerosol yield
Literature Cited (1) Siegneur, C.; Pai, P.; Louis, J. F. Review of Air Quality Models for Particulate Matter. Document CP015-97-1b; 1997. (2) Grosjean D.; Seinfeld, J. H. Atmos. Environ. 1989, 23, 17331747. (3) Odum, J. R.; Hoffmann, T.; Bowman, F.; Collins, D.; Flagan, R. C.; Seinfeld, J. H. Environ. Sci. Technol. 1996, 30, 2580-2585. (4) Odum, J. R.; Jungkamp, T. P. W.; Griffin, R. J.; Forstner, H. J. L.; Flagan, R. C.; Seinfeld, J. H. Environ. Sci. Technol. 1997, 31, 1890-1897. (5) Pankow, J. F. Atmos. Environ. 1994, 28 (2), 185-188. (6) Pankow, J. F., Atmos. Environ. 1994, 28 (2), 189-193. (7) Pandis, S. N.; Harley, R. A.; Cass, G. R.; Seinfeld, J. H. Atmos. Environ. 1992, 26A, 2269-2282. (8) Forstner, H. J. L.; Flagan, R. C.; Seinfeld, J. H. Environ. Sci. Technol. 1997, 31, 1345-1358. (9) Jang, M.; Kamens, R. M. Environ. Sci. Technol. 2001, 35, 36263639. (10) Bowman, F. M.; Odum, J. R.; Seinfeld, J. H.; Pandis, S. N. Atmos. Environ. 1997, 31, 3921-3931. (11) Atkinson, R. J. Phys. Chem. Ref. Data. 1994, Monograph 2. (12) Atkinson, R. Atmos. Environ. 2000, 34, 2063-2101. (13) Atkinson, R. A. Rev. Atmos. Envion. 1990, 24A, 1-41. (14) Eusebi, A. Ph.D. Dissertation, University of California, 1996. (15) Perry, R. A.; Atkinson, R. J.; Pitts, J. N., Jr. Phys. Chem. 1997, 81, 296-304. (16) Holes, A.; Eusebi, A.; Allen, D. T. Aerosol Sci. Technol. 1997, 26, 516-526. (17) Carter, W. P. L. Documentation of the SAPRC-99 Chemical Mechanism for VOC Reactivity Assessment. Report to California Air Resources Board; Contracts 92-329 and 95-308; 2000. (18) Bandow, H.; Washida, N. Chem. Soc. Jpn. 1985, 58, 2549-2555. (19) Bierbach, A.; Barnes, I.; Becker, K. H.; Wiesen, E. Environ. Sci. Technol. 1994, 28, 715-729. (20) U.S. Environmental Protection Agency. Software to estimate Atmospheric Oxidation Potentials and Vapor Pressures can be downloaded as EPI Suite at www.epa.gov/oppt/greenengineering; 2002. (21) Atkinson, R. J. Chem. Rev. 1985, 85, 69-201. (22) Atkinson, R. J. Int. J. Chem. Kinet. 1986, 18, 555-568. (23) Atkinson, R. J.; Aschmann, S. M. Atmos. Environ. 1987, 21, 23232326. (24) Atkinson, R. J.; Aschmann, S. M.; Arey, J. Int. J. Chem. Kinet. 1991, 23, 77-97. (25) Atkinson, R. J.; Carter, W. P. L. Chem. Rev. 1984, 84, 437-470. (26) Biermann, H. W.; MacLeod, H.; Atkinson, R. J.; Winer, A. M.; Pitts, J. N., Jr. Environ. Sci. Technol. 1985, 19, 244-248. (27) Kwok, E. S. C.; Atkinson, R. J. Environ. Sci. Technol. 1992, 26, 1798-1807. (28) Kwok, E. S. C.; Atkinson, R. J. Atmos. Environ. 1995, 29, 16851695. (29) Kwok, E. S. C. Environ. Sci. Technol. 1996, 30, 329-334. (30) Atkinson, R. J. Int. J. Chem. Kinet. 1987, 19, 799-828. (31) Meylan, W. Estimation Accuracy of the Atmospheric Oxidation Program Version 1.87; Syracuse Research Corporation, Environmental Science Center: 1998. (32) Meylan, W.; Howard, P. User’s Guide for MPBPVP version 1.2; Syracuse Research Corporation: 1996. (33) Dechapanya, W. Ph.D. Disseratation, University of Texas. 2002. (34) Carter, W. P. L. Development and Application of an Up-To-Date Photochemical Mechanism for Airshed Modeling and Reactivity Assessment; Final Report Contract A932-094; Air Resources Board Research Division: 1998.
Mint
initial amount of aerosol mass
MGLY
methylglyoxal
Pi
total amount of semivolatile particulate matter species i generated by reaction
PM
particulate matter
PMk
semivolatile product species k
∆ROG
amount of reactive organic gas reacts
RO2R
operator represents peroxy radical reactions with NO that result in NO to NO2 conversion and formation of HO2 radical
R2O2
operator represents effect of NO to NO2 conversion without HO2 formation
RO2N
operator represents reactions of peroxy radicals with consumption of NO and various types of organic nitrate formation
SOA
secondary organic aerosol
SAPRC
Statewide Air Pollution Research Center
TPM1
aerosol product with organonitrate groups from 1,3,5-trimethylbenzene
TPM2
aerosol product without organonitrate groups from 1,3,5-trimethylbenzene
TPM5
aerosol product with organonitrate groups from 1,2,4-trimethylbenzene
TPM6
aerosol product without organonitrate groups from 1,2,4-trimethylbenzene
Received for review August 28, 2002. Revised manuscript received May 5, 2003. Accepted May 16, 2003.
VOCs
volatile organic compounds
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