Uncertainties in Modeling Secondary Organic Aerosols: Three

Environ. Sci. Technol. 2003, 37, 3647-3661

Uncertainties in Modeling Secondary Organic Aerosols: Three-Dimensional Modeling Studies in Nashville/Western Tennessee B E T T Y K . P U N , * ,† S H I A N G - Y U H W U , † CHRISTIAN SEIGNEUR,† JOHN H. SEINFELD,‡ R O B E R T J . G R I F F I N , §,| A N D SPYROS N. PANDIS⊥ Atmospheric and Environmental Research, Inc., 2682 Bishop Drive, Suite 120, San Ramon, California 94583, Department of Chemical Engineering, California Institute of Technology, Pasadena, California 91125, Department of Civil and Environmental Engineering, Duke University, Durham, North Carolina 27708, and Department of Chemical Engineering, Carnegie-Mellon University, Pittsburgh, Pennsylvania 15213

The formation of secondary organic aerosols (SOA) is simulated for the Nashville/western Tennessee domain using three recent SOA modules incorporated into the threedimensional air quality model, CMAQ. The Odum/Griffin et al. and CMU/STI modules represent SOA absorptive partitioning into a mixture of primary and secondary particulate organic compounds (OC), with some differences in the formulation of the absorption process and the selection of SOA species and their precursors. Empirical representations based on measured laboratory SOA yields are used for condensable organic products in both these modules. The AEC module simulates SOA absorption into organic and aqueous particulate phases, and a representation based on an explicit gas-phase mechanism is used in the AEC module. Predicted SOA concentrations can vary by a factor of 10 or more. In general, the gas-phase mechanistic approach predicts a higher yield of SOA than those based on laboratory yields. There exist some differences in the two empirical modules despite their similar basis on experimental data. All three modules predict a dominance of SOA of biogenic origin as compared to SOA of anthropogenic origin. The causes for differences among the three SOA modules include the representation of terpenes, the mechanistic versus empirical representation of SOAforming reactions, the identities of SOA, and the parameters used in the gas/particle partitioning calculations. Two sensitivity studies show that formation of water-soluble SOA and temperature dependence may be areas of key uncertainties affecting current models. * Corresponding author phone: (925)244-7125; fax: (925)244-7129; e-mail: [email protected]. † Atmospheric and Environmental Research, Inc. ‡ California Institute of Technology. § Duke University. | Present address: Institute for the Study of Earth, Oceans, and Space and Department of Earth Sciences, University of New Hampshire, Durham, NH 03824. ⊥ Carnegie-Mellon University. 10.1021/es0341541 CCC: $25.00 Published on Web 07/12/2003

 2003 American Chemical Society

Introduction Fine particulate matter with aerodynamic diameter less than 2.5 µm (PM2.5) is of research and regulatory interest because of the impacts that such particles have on human health, acid deposition, atmospheric visibility, and the earth’s radiative budget. On a regional basis, secondary components of PM2.5, including sulfate, nitrate, ammonium, and organic compounds (OC), typically constitute the majority of the PM2.5 mass. Of these components, current understanding is least complete regarding organic aerosols. Organic aerosols consist of both primary and secondary compounds. The secondary component of organic aerosols is referred to as secondary organic aerosol (SOA). There exist a number of laboratory studies aimed at determining the aerosol-forming potential of individual hydrocarbons (e.g., refs 1-10). In addition, several modules have been developed for the prediction of SOA formation in atmospheric models (e.g., refs 11-19). SOA formation depends on the atmospheric mix of anthropogenic and biogenic precursors, the gas-phase oxidation of these precursors to lead to condensable products, and the subsequent gas/particle partitioning of these products. We assess here three recent SOA modules embedded in a state-of-theart three-dimensional (3-D) atmospheric model with the goal of determining the degree of agreement of the modules and the mechanistic causes of any discrepancies. This evaluation will serve as a guide for continued development of the capability to predict SOA formation in atmospheric models. The three SOA modules compared here are as follows: (i) a SOA module based on smog chamber data obtained at the California Institute of Technology (Caltech) by Odum et al. (2) and Griffin et al. (4), (ii) the Carnegie Mellon University (CMU) and Sonoma Technology, Inc. (STI) SOA module (11, 12) that is also based on smog chamber data, and (iii) a detailed SOA module based on full gas-phase chemistry and organic and aqueous phase partitioning (17). Hereafter, we will refer to these modules as Odum/Griffin et al. (O/G), CMU/STI, and AER/EPRI/Caltech (AEC). Although the first two modules are derived from similar sets of experimental data, they differ in their empirical representations of SOA formation; therefore, their comparison provides a measure of the uncertainties associated with the derivation of model representations of experimental data sets. These three SOA modules are incorporated into the 3-D Community Multiscale Air Quality model (CMAQ). The test bed for the comparison is a 3-D simulation of the Nashville/western Tennessee domain over a 5-day episode in July 1995.

Description of the SOA Modules CMAQ (20) was selected as the host air quality model because its modularity facilitates the incorporation of new atmospheric modules. The O/G and CMU/STI modules were integrated into the modal aerosol representation contained within CMAQ, whereas the AEC module was incorporated into a new sectional aerosol module described below. For each SOA module, a corresponding gas-phase mechanism is needed to predict the rate of formation of condensable products. Odum/Griffin et al. (O/G). In the analysis of laboratory chamber SOA yields for approximately 40 individual parent organic species, it was found that the final experimental SOA yield can be fit with an empirical model that assumes two condensable products for each parent molecule. Each product is characterized by a gas/particle partitioning parameter and a mass stoichiometric factor. In the implementation of the O/G model, which is based on these VOL. 37, NO. 16, 2003 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

9

3647

TABLE 1. Modifications to CBM-IV for the O/G SOA Module anthropogenic reactions with new productsa TOL + OH f 0.08 XO2 + 0.36 CRES + 0.44 HO2 + 0.56 TO2 + 0.071 TOLAER1 + 0.138 TOLAER2 XYL + OH f 0.7 HO2 + 0.5 XO2 + 0.2 CRES + 0.8 MGLY + 1.1 PAR + 0.3 TO2 + 0.038 XYLAER1 + 0.167 XYLAER2 new biogenic reactionsa

rate constants (21) (cm3 molecule-1 s-1)

CAR + OH f 0.054 CARAER1 + 0.517 CARAER2 + OH CAR + O3 f 0.128 CARAER3 + 0.068 CARAER4 + O3 CAR + NO3 f 0.743 CARAER5 + 0.257 CARAER6 + NO3 CRP + OH f 1.0 CRPAER + OH HUM + OH f 1.0 HUMAER + OH LIM + OH f 0.239 LIMAER1 + 0.363 LIMAER2 + OH LNL + OH f 0.073 LNLAER1 + 0.053 LNLAER2 + OH OCI + OH f 0.045 OCIAER1 + 0.149 OCIAER2 + OH APIN + OH f 0.038 APINAER1 + 0.326 APINAER2 + OH APIN + O3 f 0.125 APINAER3 + 0.102 APINAER4 + O3 BPIN + OH f 0.13 BPINAER1 + 0.0406 BPINAER2 + OH BPIN + O3 f 0.026 BPINAER3 + 0.485 BPINAER4 + O3 BPIN + NO3 f 1.0 BPINAER5 + NO3 SAB + OH f 0.067 SABAER1 + 0.399 SABAER2 + OH SAB + O3 f 0.037 SABAER3 + 0.239 SABAER4 + O3 SAB + NO3 f 1.0 SABAER5 + NO3 TER + OH f 0.091 TERAER1 + 0.367 TERAER2 + OH TPO + OH f 0.049 TPOAER1 + 0.063 TPOAER2 + OH TPL + OH f 0.046 TPLAER1 + 0.034 TPLAER2 + OH

8.8 × 10-11 3.7 × 10-17 9.1 × 10-12 1.97 × 10-10 2.93 × 10-10 1.71 × 10-10 1.59 × 10-10 2.52 × 10-10 5.37 × 10-11 8.66 × 10-17 7.89 × 10-11 1.36 × 10-17 2.31 × 10-12 1.17 × 10-10 8.6 × 10-17 1.0 × 10-11 2.7 × 10-10 1.59 × 10-10 2.25 × 10-10

biogenic compd (MW) carene (136) caryophyllene (204) humulene (206) limonene (136) linalool (154) ocimene (136) R-pinene (136) β-pinene (136) sabinene (136) terpinene (136) terpinenol (154) terpinolene (136)

a Mass-based stoichiometric factors (R ) in Odum et al. (2) and Griffin et al. (4) are used as stoichiometric coefficients (mole products/mole i reactants), implicitly assuming that the surrogate condensable products share the same MW as their precursors. (Mathematically, a lower stoichiometric coefficient may be used if a higher MW is assumed for the product, and vice versa, so that the mass of condensable products formed per mass of precursors is consistent with the experimental R values.)

experimentally determined product parameters, the CarbonBond Mechanism Version IV (CBM-IV) was modified to represent the formation of condensable products. Two condensable products were added to the existing reactions of each of the aromatic species: TOLAER1 and TOLAER2 (high aerosol yield products) for toluene oxidation and XYLAER1 and XYLAER2 (low aerosol yield products) for xylene oxidation. The mass stoichiometric factors determined experimentally (2) are implemented as molar stoichiometric coefficients in the reactions listed in Table 1. Mathematically, this implementation is equivalent to assuming the same molecular weight (MW) for the assumed condensable products and their precursors. Griffin et al. (4) applied the same partitioning model to biogenic compounds and determined the stoichiometric coefficients and gas/particle partitioning parameters of 34 surrogate condensable products from 12 biogenic precursor compounds. The new biogenic reactions generating condensable products are listed in Table 1 together with the kinetic rate constants compiled in Lamb et al. (21). Since aromatic and biogenic compounds are already represented in the original lumped structure formulation of CBM-IV to simulate O3 formation, it is important that the reactions added for biogenic SOA formation do not alter O3 chemistry. Accordingly, OH, O3, and NO3 are artificially regenerated in the reactions forming SOA so that the addition of these reactions has no net effect on the original O3 chemistry. In the O/G module, the gas/particle partitioning of each of the 38 condensable species is governed by

Ai/Msum Ki ) Gi

(1)

where Ki (m3/µg) is the partition coefficient, the value of which is obtained from the laboratory SOA yield data (see Table 2), Ai (µg/m3 air) is the mass concentration of species i in the particulate phase, Gi (µg/m3 air) is the mass concentration of species i in the gas phase, and Msum (µg/m3 air) is the sum of primary OC (nonvolatile) and secondary 3648

9

ENVIRONMENTAL SCIENCE & TECHNOLOGY / VOL. 37, NO. 16, 2003

TABLE 2. Partition Constants of the O/G SOA Module (2, 4) condensable speciesa

K (m3/µg)b

condensable speciesa

K (m3/µg)b

TOLAER1 TOLAER2 XYLAER1 XYLAER2 CARAER1 CARAER2 CARAER3 CARAER4 CARAER5 CARAER6 CRPAER HUMAER LIMAER1 LIMAER2 LNLAER1 LNLAER2 APINAER1 APINAER2 APINAER3 APINAER4

0.053 0.0019 0.042 0.0014 0.043 0.0042 0.337 0.0036 0.0088 0.0091 0.0416 0.0501 0.055 0.0053 0.049 0.0210 0.171 0.0040 0.088 0.0788

BPINAER1 BPINAER2 BPINAER3 BPINAER4 BPINAER5 SABAER1 SABAER2 SABAER3 SABAER4 SABAER5 OCIAER1 OCIAER2 TPOAER1 TPOAER2 TERAER1 TERAER2 TPLAER1 TPLAER2

0.044 0.0049 0.195 0.0030 0.0163 0.258 0.0038 0.819 0.0001 0.0115 0.174 0.0041 0.159 0.0045 0.081 0.0046 0.185 0.0024

a MW of products assumed to be equal to their precursors (see Table 1) based on the use of mass-based stoichiometric factors of surrogate products as mole based stoichiometric coefficients in mechanisms. b At experimental temperatures ranging from 301 to 316 K, average experimental temperature ) 310 K.

OC (semivolatile) in the particulate phase that serve as the organic absorbing medium. The smog chamber experiments from which Ki and stoichiometric coefficients are derived were conducted at temperatures generally higher (301-316 K) than typical ambient temperatures. On the basis of Pankow (22), Ki can be related to temperature and saturation vapor pressure, which is also a function of temperature. The ClausiusClapeyron equation is used to account for the temperature dependence of the saturation vapor pressure, using a value

TABLE 3. Modifications to CBM-IV for the CMU/STI SOA Module anthropogenic and biogenic reactions with new productsa PAR + OH f 0.87 XO2 + 0.13 XO2N + 0.16 HO2 + 0.11 ALD2 + 0.76 ROR + (-0.11) PAR + 3.4 × 10-4 CG4 OLE + OH f FORM + ALD2 + XO2 + HO2 + (-1) PAR + 5.6 × 10-4 CG4 OLE + O3 f 0.5 ALD2 + 0.74 FORM + 0.33 CO + 0.44 HO2 + 0.22 XO2 +0.1 OH + (-1) PAR + 5.6 × 10-4 CG4 OLE + O f 0.63 ALD2 + 0.38 HO2 + 0.28 XO2 + 0.3 CO + 0.2 FORM + 0.02 XO2N + 0.22 PAR + 0.2 OH + 5.6 × 10-4 CG4 OLE + NO3 f 0.91 XO2 + 0.02 XO2N + ALD2 + FORM + (-1) PAR + NO2 + 5.6 × 10-4 CG4 TOL + OH f 0.08 XO2 + 0.36 CRES + 0.44 HO2 + 0.56 TO2 + 0.044 CG1 + 0.092 CG2 XYL + OH f 0.7 HO2 + 0.5 XO2 + 0.2 CRES + 0.8 MGLY + 1.1 PAR + 0.3 TO2 + 0.030 CG1 + 0.12 CG3 + 0.1 BZA CRES + OH f 0.4 CRO + O.6 XO2 + 0.6 HO2 + 0.3 OPEN + 0.029 CG4 CRES + NO3 f CRO + HNO3 + 0.029 CG4 CRO + NO2 f NTR + NPHN TERP + OH f OH + 0.075 CG5 + 1.0 × 10-5 CG6 TERP + NO3 f NO3 + 0.075 CG5 + 1.0 × 10-5 CG6 TERP + O3 f O3 + 0.075 CG5 + 1.0 × 10-5 CG6 new reactionsa

rate constants (25) (cm3 molecule-1 s-1)

BZA + OH f 6.5 × 10-4 CG4 + BZO2 + OH BZA + NO3 f 6.5 × 10-4 CG4 + BZO2+ NO3 BZO2 + NO f PHO + NO BZO2 + HO2 f PHEN + HO2 BZO2 + C2O3 f PHEN + C2O3 BZO2 + XO2 f PHEN + XO2 PHO + HO2 f PHEN + HO2 PHO f PHEN PHO + NO2 f NPHN + NO2 PHEN + OH f 0.022 CG4 + OH PHEN + NO3 f 0.022 CG4 + NO3 NPHN + NO3 f 0.049 CG4 + NO3

1.36 × 10-11 1.40 × 10-12 (T/300)-1 e-1886/T 2.52 × 10-12 3.40 × 10-13 (T/300)-1 e800/T 2.80 × 10-12 (T/300)-1 e530/T 1.86 × 10-11 (T/300)-1 e530/T 3.40 × 10-13 (T/300)-1 e800/T 1.0 × 10-3 1.36 × 10-11 2.63 × 10-11 (T/300)-1 3.59 × 10-12 (T/300)-1 3.59 × 10-12 (T/300)-1

new secondary compounds benzaldehyde peroxybenzyl radical

phenoxy radical phenol nitrophenol a

Stoichiometric coefficients determined from experimental mass stoichiometric factors (R) based on assumed MW of surrogate condensable products (see Table 4) and MW of precursors.

of 72.7 kJ/mol for the enthalpy of vaporization (∆Hvap). The value for ∆Hvap corresponds to the arithmetic mean of available literature data on such compounds (23, 24). Theoretically, Msum varies as the concentrations Ai vary. Therefore, an iterative procedure would be necessary to solve the partitioning equations simultaneously for all condensing species. For efficient implementation in a 3-D model, however, Msum is calculated based on the concentration and composition of PM2.5 at the beginning of the time step in each grid cell (typically on the order of 6 min) and held constant for the partitioning calculation. The system of 38 equations is therefore decoupled and can be solved explicitly as follows:

Ai )

CiKiMsum (1 + KiMsum)

(2)

where Ci is the total concentration (µg/m3 air) of species i available for partitioning. Decoupling the calculation of Msum from the determination of Ai is an appropriate procedure when Msum is dominated by primary organic aerosols or if the concentration and composition of organic particles change slowly with time. CMU/STI. Like the O/G module, the CMU/STI module is also formulated on the basis of empirical SOA formation data from smog chambers. In Strader et al. (11), SOA formation from alkanes, alkenes, aromatics, secondary aromatic products (such as phenol, cresol, and nitrophenol), and terpenes is represented. Since CBM-IV does not explicitly treat alkane and alkene species, it was necessary to map individual species to the CBM-IV functional groups. The formation of condensable compounds from alkanes and alkenes was represented in the reactions of paraffins (PAR) with OH and olefins (OLE) with OH, O3, and O, respectively. In these reactions, the yields of the condensables take into account the fraction of PAR and OLE that represents higher alkanes and alkenes (since short-chained alkanes and alkenes

do not form SOA) based on the composition of ambient VOC in the Nashville area and the SOA yields of higher alkanes and alkenes. Condensable products were included in the toluene (TOL), xylene (XYL), and cresol (CRES) reactions. New reactions include those of benzaldehyde (BZA) and radicals derived from it and the reactions of phenol (PHEN) and nitrophenol (NPHN). As noted earlier, radicals and O3 are regenerated in these new reactions, so that they do not affect O3 chemistry. These new reactions were derived from CBM-X (25), the more comprehensive version of CBM-IV. Terpenes are represented by one model class (TERP), which forms two condensable products. In total, six condensable products are used to represent SOA formation. Aerosol yields from Strader et al. (11) have been updated in the current application. The updated aerosol yields are lower and more consistent with available experimental data. Table 3 shows the implementation of SOA reactions in the CBM-IV mechanism. Although both the O/G and the CMU/STI modules represent the same underlying physical process for the gas/ particle partitioning of condensable products, there are some differences in the implementation of the absorption modules. In the CMU/STI module, a pseudo-ideal solution and Raoult’s law are assumed for the organic particulate phase. Therefore, the partition relationships are defined based on the saturation vapor pressure of the condensable species, and the gas-phase concentration is defined to be equal to the product of the species mole fraction in the liquid phase and the pure liquid saturation vapor pressure:

Gi )

Ai/MWi

c 0i ) xi c 0i

n

∑A /MW j

(3)

j

j)1

where MWi is the molecular weight of species i, xi is the mole fraction of i in the liquid phase, and c0i is the pure liquid VOL. 37, NO. 16, 2003 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

9

3649

TABLE 4. Assumed MW and Saturation Concentrations of Condensable Compounds in the CMU/STI Module (11)

a

condensable species

assumed MW

saturation concn (µg m-3)a

CG1 CG2 CG3 CG4 CG5 CG6

150 140 163 140 180 180

18.9 470 640 5.7 6.6 6.6

At 313 K.

saturation vapor concentration (in µg/m3) (Table 4). Mole fractions are defined based on condensable compounds and primary (nonvolatile) OC and SOA dissolved in a solution of primary and secondary compounds. The inclusion of primary OC as part of the absorbing medium is an update from Strader et al. (11, 12). The effect of primary organics on the predictions of the absorptive partitioning modules is discussed later. In eq 3, MWi/c0i plays a role analogous to that of Ki in the O/G formulation. c0i is a function of temperature according to the Clausius-Clapeyron equation, with ∆Hvap ) 70 kJ mol-1, a value that is updated from Strader et al. (11, 12). The module is mathematically formulated in a manner similar to that of the O/G module, and the denominator of eq 3 is treated as constant for a given time step in the partition calculation. AEC. The Caltech Atmospheric Chemistry Mechanism (CACM) contains 361 gas-phase reactions among 189 species and provides detailed descriptions of the chemistry of alkanes (3 classes), alkenes (2 classes), aromatics (2 classes), alcohols (3 classes), isoprene, and terpenes (2 classes) (26). Several generations of products are described, including the formation of highly polar products with multiple functional groups. In the past, gas-phase mechanisms have been designed to describe O3 formation, and SOA constituents have typically not been modeled in detail. CACM is designed for modeling O3 and SOA because 42 condensable, multifunctional secondand third-generation products are explicitly represented, including 14 with sufficient solubility to dissolve into aqueous particles. The AEC SOA module (17, 27) simulates an external mixture of hydrophilic and hydrophobic particles. Hydrophilic condensable OC partition into existing aqueous particles when liquid water is available (e.g., associated with inorganic components). This partition is governed by Henry’s law and takes into account the activity coefficient of the molecular solute, which is calculated using UNIFAC (28). The dissociation of condensable OC with acidic functional groups increases their partition into the aqueous phase. When no liquid water is available, hydrophilic condensable products will undergo absorption, similar to the partition of hydrophobic compounds (discussed below). Hydrophilic SOA are associated with additional aerosol water when the ambient relative humidity is above the deliquescence humidity of any individual organic compound in the particulate phase. The addition of organic ions changes the pH and total water content of aqueous particles and, consequently, affects the partition of inorganic compounds, such as nitrate and ammonium. Therefore, this organic module is coupled to the inorganic equilibrium partition model ISORROPIA (29) to simulate organic-inorganic interactions. Hydrophobic condensable OC are absorbed into particles and are described based on equilibrium gas/particle partitioning between the particulate phase and the gas phase, as represented in eq 1. The particulate-phase concentration Ai is calculated based on the partition coefficient Ki, gasphase concentration Gi, and Msum (the sum of SOA and any primary organic material that may serve as the absorbing 3650

9


organic medium). The partition coefficient is calculated using (22)

Ki )

760RT 106Psat i γi(MWom)

(4)

Key parameters include the saturation vapor pressure (Psat i in Torr) of the partitioning compounds, the UNIFAC-derived activity coefficient (γi) of the partitioning compound in the particulate phase, and the average molecular weight (MWom) of the organic material into which the condensable compound is absorbed. Both the hydrophilic and hydrophobic SOA modules require the simultaneous solution of partitioning equations for several compounds. A globally convergent Newton/line search method is implemented in the AEC module. In the interest of computational efficiency in 3-D applications, some simplifications are provided as options. The amount of water associated with hydrophilic OC is calculated by assuming that the water associated with each hydrophilic compound in a binary solution is additive [i.e., using the ZSR (Zdanovskii, Stokes, and Robinson) equation (30)]. In the hydrophobic module, Msum in eq 1 is calculated based on the concentration and composition of PM at the beginning of the time step in each grid cell and held constant for the partitioning calculation. A surrogate compound approach is used for the interface of AEC with CACM. We represented the 42 condensable organic products of CACM using 10 surrogate compounds, grouped according to their affinity (5 surrogate species for 28 explicit hydrophobic compounds and 5 surrogate species for 14 hydrophilic compounds), origin (anthropogenic vs biogenic), size (number of carbons), volatility, and dissociation properties (17). Each surrogate compound assumes the characteristic size and functional groups of the explicit compounds it represents. Because of the paucity of property data for complex OC, partitioning parameters, such as Henry’s law constants and saturation vapor pressures, are estimated using group contribution methods (17). Dissociation constants and deliquescence relative humidities are assigned based on analogy to simpler compounds. The AEC SOA module is implemented within Version 2 of the Model for Aerosol Dynamics, Reaction, Ionization, and Dissolution (MADRID 2), a sectional PM model developed at AER (31, 32). This model is comprehensive and provides different options for simulating various processes described in the aerosol dynamics equation. For 3-D modeling, the options selected represent a compromise between computational speed and accuracy. For example, only two sections are used to represent fine and coarse PM. Secondary organic components are assumed to partition only into the fine section (PM2.5), an assumption that is consistent with the empirical modules. Dry deposition of condensable gases and organic particles is included in the simulations. Condensable gases typically contain multiple functional groups, such as aldehyde, acid, and alcohol groups. Without information on the identities of the condensable compounds in the O/G and CMU/STI formulations, deposition velocities of the organic gases are assumed to be analogous to that of higher aldehydes. For the condensable products formed in CACM, secondary condensable compounds were assigned dry deposition velocities based on their major functional groups, following one of four classes of compounds: aromatics, acids, aldehydes, and nitrates (in preferential order). OC in particles are assumed to be removed by dry deposition at the same rate as PM2.5 particles. Therefore, the calculation of the deposition velocity of PM2.5 was modified in CMAQ to include the additional organic particulate species.

FIGURE 1. Simulation domain. LBL is Land Between Lakes; NAS is Nashville; YOU is Youth, Inc.

Simulations of the Nashville/Tennessee Region Air quality simulations of the Nashville/western Tennessee region were conducted for O3 and PM using CMAQ (20). This domain (Figure 1) was selected for the study of SOA formation because the formation of both anthropogenic and biogenic SOA is important in this region; biogenic emissions are quite abundant within the domain, while anthropogenic VOC are emitted in urban areas such as Nashville, TN, and from major point sources. The PM simulation builds upon the O3 base case simulation (33, 34) for July 14-18, 1995. The PM simulation domain contains 100 × 65 grid cells, with a horizontal resolution of 4 km and 15 layers, with finer vertical resolution near the ground. Except for inputs that are chemical mechanism specific (VOC speciation), the same inputs were used for all simulations. Meteorological inputs were generated from an MM5 simulation conducted with four-dimensional data assimilation (34). Initial conditions, boundary conditions, and emissions inputs were amended for PM and PM precursors. For PM species, initial and boundary conditions were defined using data from IMPROVE network sites surrounding the modeling domain, including Great Smoky Mountains National Park, Mammoth Cave National Park, Shining Rock Wilderness, Upper Buffalo Wilderness, and Sipsey Wilderness (http://vista.cira.colostate.edu/improve/Data/IMPROVE/ improve_data.htm). Two of the IMPROVE sites also measured SO2; these measurements were used to define the boundary conditions for SO2. A simulation with zero initial and boundary conditions was performed to establish typical concentrations due to emissions within the domain. These concentrations were used to define the initial and boundary conditions for NH3, isoprene, and SO2, where appropriate. Terpene concentrations were assumed to be negligible at the boundary. Biogenic emissions were obtained from the Biogenic Emission Inventory System, Version 2 (BEIS 2; 35). The O/G approach requires detailed terpene speciation, and the AEC module requires two classes of terpenes. Limited information is currently available for terpene speciation. We used a composition based on Helmig et al. (36) for an Atlanta forest to speciate BEIS 2 terpene emissions and grouped the resulting species into a high yield group and a low yield group for use in CACM/AEC. Despite differences in the speciation, the models shared the same total amount of terpenes emitted and the same spatial distribution of terpene emissions. CACM also requires more detailed anthropogenic organic speciation than CBM-IV for aliphatic compounds such as alkanes and alkenes. We speciated the anthropogenic OC based on a typical speciation profile (18). The same emissions were used in all three models for aromatic compounds. All model simulations were conducted on a Sun Ultra workstation. The simulations of the 5-day episode were completed in 80 and 67 CPU hours, respectively, with the

FIGURE 2. Temporal O3 profiles in Nashville, TN (NAS) and at Youth, Inc. (YOU), on July 16-18, 1995. O/G and CMU/STI models. The CPU requirements of the AEC module were significantly higher at close to 500 CPU hours because of a more complex gas-phase chemical mechanism and the solution of simultaneous equations in the AEC module.

Evaluation of Predictions of SOA Formation The major goal is to compare predictions of SOA levels and distributions as predicted by the three modules over the Nashville region. Before the analysis of SOA formation, predictions are compared to available data on O3, PM2.5, and PM sulfate concentrations to ensure reasonable model performance. All three models perform well for O3 concentrations (Figure 2). The performance for PM2.5 and PM sulfate is commensurate with the expectations of current PM models. CACM tends to predict higher O3 than CBM-IV possibly due to more detailed treatments of biogenic compounds in CACM than in CBM-IV. In addition, the treatment of several generations of organic products allows for more NO to NO2 conversions from a single precursor molecule than the CBMIV mechanism. Measurements showed a domain maximum O3 concentration of 124 ppb at Youth, Inc. (a youth camp 16 miles southeast of Nashville) on July 16, 1995. The predicted maximum concentrations were 105 ppb for CBM-IV and 118 ppb for CACM. Figures 3a and 3b show the spatial distributions of the 24-h average PM2.5 concentrations on July 16, 1995, predicted by the CBM-IV simulation with O/G and CACM with MADRID 2, respectively. The spatial PM2.5 distribution predicted with the CMU/STI module is very similar to that shown in Figure 3a. Although MADRID 2 typically predicted higher PM2.5 concentrations, all modules show similar spatial distributions, featuring a gradient of decreasing concentrations from northeast to southwest. During the Metropolitan Acid Aerosol Characterization Study (MAACS) (37), six stations were operational in Nashville in the summer of 1995 to measure 24-h average PM10, PM2.5, and PM sulfate. The ranges of 24-h average PM2.5 values observed during the period were 17 (July 18, 1995) to 36 µg/m3 (July 15, 1995). Table 5 lists the average observed and predicted concentrations of PM2.5 and sulfate in Nashville. A range of 7 (July 17, 1995) to 16 µg/m3 (July 16, 1995) was observed for particulate sulfate during MAACS. With an average concentration of 8-9 µg/m3 for the O/G, CMU/STI, VOL. 37, NO. 16, 2003 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

9

3651

TABLE 6. Summary of 24-h Average SOA Predictions (µg m-3) at Three Sites: Land between Lakes (LBL), Nashville (NAS), and Youth, Inc. (YOU) O/G

July 16, 1995 July 17, 1995 July 18, 1995 average

FIGURE 3. Spatial distribution of 24-h average PM2.5 on July 16, 1995 (a), O/G SOA module and (b) MADRID 2/AEC.

TABLE 5. Measured vs Predicted 24-h Average PM2.5 and Sulfate Concentrations in Nashville, TN PM2.5 (µg/m3)

sulfate (µg/m3)

observeda predicted (O/G) predicted (CMU/STI) predicted (AEC)

July 16, 1995 23.6b 18.5 18.4 22.8

8.3c 7.9 7.9 8.1


July 17, 1995 19.0d 17.6 17.5 23.9

6.8e 8.2 8.2 8.5


July 18, 1995 18.3 f 21.4 21.3 25.5

7.3g 9.0 9.1 8.8

a Average of available sites in the Nashville area (37). b Range at five sites: 20.4-29.5. c Range at six sites 8.2-15.2. d Range at two sites 18.819.0. e Range at two sites 6.4-6.8. f Range at six sites 16.7-24.1. g Range at six sites 6.6-8.7.

and MADRID 2 simulations, sulfate predictions are also consistent with the observations (37) during MAACS. The significant day-to-day variations in PM2.5 and sulfate concentrations may not be well-reproduced due to uncertainties in the boundary conditions for PM. Because of a lack of data, constant boundary conditions were assumed. The relatively long atmospheric residence time of fine particles in the absence of precipitation implies that the boundary conditions will affect the fine particle concentrations throughout the entire modeling domain. 3652

9


CMU/STI

AEC

LBL

NAS YOU

LBL

NAS YOU

LBL

NAS YOU

0.20 0.20 0.26 0.22

0.23 0.30 0.23 0.25

0.14 0.14 0.18 0.15

0.15 0.18 0.16 0.16

1.98 1.85 2.58 2.14

2.29 2.82 2.64 2.58

0.19 0.28 0.18 0.22

0.14 0.18 0.13 0.13

1.88 2.52 1.97 2.12

Figure 4 shows the predicted composition of PM2.5 in Nashville. The O/G and CMU/STI approaches give very similar composition. By design, these two simulations only differ in the formation of SOA. The small predicted SOA concentrations have negligible effects on other processes (e.g., PM deposition). In the MADRID 2 simulation, differences in sulfate, ammonium, and nitrate predictions are due to a combination of different gas-phase mechanisms, different inorganic partition modules, and different dry deposition algorithms used in the sectional and modal approaches. Furthermore, the inclusion of hydrophilic SOA in aqueous particles also serves to enhance the partition of ammonium nitrate in MADRID 2 due to additional particulate water associated with SOA. SOA is predicted to account for a small fraction of PM2.5 mass in Nashville: 1% in the O/G and CMU/STI simulations and 10-11% in the MADRID 2 simulation (Figure 4). Total SOA accounts for some of the differences in the PM2.5 predictions of the three modules. The 24-h average SOA contribution as predicted by the AEC module was significantly higher in Nashville than that predicted by the CMU/STI and O/G modules (2.3-2.8 µg m-3 vs 0.1-0.3 µg m-3, respectively). The spatial distributions of 24-h average SOA concentrations on July 16, 1995 as predicted by the three SOA modules are presented in Figure 5. Different concentration scales are used in these spatial plots because of the differences in the simulated SOA concentrations. On July 16, the domain average 24-h SOA concentrations were 0.15, 0.11, and 1.48 µg m-3, respectively, for the O/G, CMU/STI, and AEC modules. Spatial patterns were qualitatively similar with gradients of SOA concentrations in all three cases decreasing from north to south, indicating the general buildup of SOA in the direction of wind flows. SOA tended to be widespread, an indication of the predominance of biogenic SOA, with more widespread precursor sources than anthropogenic compounds. We selected three sites for further analyses: an urban location, Nashville (NAS); a semirural location that is at times affected by the Nashville urban plume, Youth, Inc. (YOU); and a rural location, Land Between Lakes (LBL). The average SOA concentrations predicted by the three modules at these three sites are summarized in Table 6. On average, NAS was predicted to have the highest SOA concentrations of the three sites. The O/G module predicted higher concentrations of SOA at NAS than at YOU and LBL on the first two days. The CMU/STI module predicted slightly higher SOA at NAS on the first day than LBL and YOU. On the second day, SOA predictions were similar at NAS and YOU, which were higher than LBL. On the last simulation day, the SOA concentration at LBL exceeded that at the other two sites for both O/G and CMU/STI modules. The AEC module predicted the highest SOA at NAS on all three days. The three SOA modules predicted slightly different geographical distributions of SOA, and these differences were time-dependent. Different amounts of condensable OC and different mixtures of SOA are predicted by the three modules. On the

FIGURE 4. 24-h average composition of PM2.5 components in Nashville on July 16 and July 18, 1995. OC is primary organic carbon, EC is elemental carbon, Anth_SOA is anthropogenic SOA, and Bio_SOA is biogenic SOA predicted by the O/G module (top), the CMU/STI module (middle), and the MADRID 2/AEC module (bottom). other hand, the composition and partitioning characteristics of SOA predicted by each module were quite similar at locations ranging from urban to rural. As shown in Figure 5 for each module, SOA tend to be regionally distributed, a characteristic also shared by the condensable gases that are in equilibrium with SOA. Therefore, the composition of SOA and condensable gases was more spatially homogeneous than the organic precursor emissions. For example, condensable (gas + particles) products of biogenic origin were predicted by the O/G module to account for 78% of the total (anthropogenic + biogenic) condensable products at LBL and 75% in Nashville. The analogous values were 37% at LBL

and 30% in Nashville with the CMU/STI module and 84% at both LBL and NAS with the hydrophobic condensables in the AEC module. These results indicate that SOA formation is predicted to take place on a regional scale by all three models. The predicted yield of SOA is governed by the particular SOA precursors (which compounds produce SOA and how they are represented, the latter is especially important for biogenic compounds), the gas-phase mechanism of formation of condensable compounds, and gas/particle partitioning of OC. The differences in SOA predictions by the three modules are now addressed. VOL. 37, NO. 16, 2003 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

9

3653

FIGURE 5. Spatial distribution of 24-h average SOA predicted by (a) O/G, (b) CMU/STI, and (c) AEC SOA modules on July 16, 1995. Origins of SOA. Different anthropogenic and biogenic SOA precursors and condensable products are included in the three modules. CACM and CMU/STI represent several classes of anthropogenic precursors, including alkanes, alkenes, and aromatic compounds. O/G includes SOA production from aromatic compounds but not from the other anthropogenic compounds listed above. SOA formation from 12 terpene compounds is represented in O/G; CACM lumps monoterpenes in a high SOA yield group and a low SOA yield group, while CMU/STI represents only one monoterpene species. In the modified CBM-IV mechanisms, only the final generation of condensable products is predicted, and the yields of condensable products are determined from smog 3654

9


chamber measurements. CACM utilizes a mechanistic approach and represents several generations of condensable products. Although uncertainties in the mechanism are significant because the reactions of complex secondary OC are not well-known, this level of mechanistic detail may eventually be necessary for the accurate representation of condensable OC. Significant differences, as illustrated in Figures 6 and 7, are observed in both the total amount and the anthropogenic or biogenic origins of the SOA predicted by the three modules. The O/G module predicts that biogenic SOA are the dominant component of total SOA in this region, accounting for about 90% of SOA at most locations. The CMU/STI simulation predicts less SOA formation than the O/G approach but a larger fraction of anthropogenic SOA, ranging from 30% at the rural site to >40% at the NAS and YOU. The AEC module predicts that about 84-87% of the total SOA can be traced to biogenic precursors at these sites. The biogenic dominance is akin to the prediction of the O/G module. Note that despite these differences, the temporal behavior of SOA concentrations is consistent among all three modules. SOA Precursors. A main difference between the two empirical models is that the formation of SOA from more classes of anthropogenic precursors is considered in the CMU/STI module than the O/G module. In the O/G module, anthropogenic SOA originate only from primary aromatic compounds. At the rural and urban locations considered, 0.02-0.03 µg m-3 anthropogenic SOA is formed. In CMU/ STI, alkanes, alkenes, and secondary aromatic compounds also produce SOA. Anthropogenic SOA accounts for 0.05 µg m-3 at LBL and 0.06 µg m-3 at NAS and YOU. In the CMU/STI module, condensable products from alkanes, alkenes, and secondary aromatic compounds are lumped together as CG4, which forms SOA4. Approximately 0.03-0.04 µg m-3 of SOA4 is formed in the CMU/STI simulation, which accounts for much of the difference in anthropogenic SOA predictions between the CMU/STI module and the O/G module. Therefore, the consideration of more anthropogenic precursors in the CMU/STI model leads to larger amounts of anthropogenic SOA relative to the O/G module. Production of Condensable Compounds. The total condensable compounds (sum of SOA and condensable gases that may be converted to SOA under favorable partitioning conditions) of each of the three modules may be used to estimate the SOA production potential. In each mechanism, the representation of parent hydrocarbons and condensable oxidation products is different. This leads to fundamental differences in the chemistry of OC leading to the production of condensable compounds. A comparison of the total gasand particle-phase concentrations of condensable compounds is used to illustrate these fundamental differences in the chemistry, irrespective of the partitioning process. On July 16, 1995, the CBM-IV/O/G model predicted the formation of 1.0-1.3 µg m-3 condensable gases at LBL, NAS, and YOU. CBM-IV with CMU/STI predicted 0.4-0.7 µg m-3 condensable gases at these sites. CACM with AEC predicted substantially more condensable gases, especially at the rural LBL site (9 µg m-3). Considering the sum of SOA and condensable gases as an SOA formation potential, at the rural site, CACM produced 7-9 times the condensable products produced in O/G and 15-18 times those produced in CMU/ STI (see Table 7). At urban NAS, total condensable products predicted by AEC are a factor of 5-7 higher than those predicted by O/G and a factor of 10-15 higher than those predicted by CMU/STI. In general, models that produce more condensable products also produced more SOA, although the ratio of SOA to total condensable compounds differs in each model because of different partitioning characteristics, as discussed in the Implementation of Gas/Particle Partitioning Theory subsection.

FIGURE 6. SOA and condensable gases predictions at NAS and LBL by (a) O/G, (b) CMU/STI, (c) AEC, and (d) hydrophobic module of AEC. Comparing the Formation of Condensable Products between the Two Empirical Models. The additional anthropogenic SOA precursors considered in CMU/STI, discussed above, lead to higher SOA formation and higher concentrations of anthropogenic condensable gases as compared to O/G. Both O/G and CMU/STI modules were formulated based on similar smog chamber data for aromatic compounds, and the SOA production potential from these compounds are very similar. For example, in the CMU/STI mechanism, each mole of TOL reacting with OH produces 0.044 mol of CG1 (MW 150) and 0.092 mol of CG2 (MW 140), which convert to 6.6 g of CG1 and 12.9 g of CG2. The TOL + OH reaction gives 0.071 mol of TOLAER1 and 0.138 mol of TOLAER2 per mol of TOL reacted in the O/G module. In the O/G model, a MW of 92 is used for all toluene products. The yields of TOLAER1 and TOLAER2 per mole of TOL are slightly lower at 6.5 and 12.7 g, respectively. Similarly, the yields of condensable products from the lower yield aromatic precursor (XYL) are 4.5 and 19.7 g in the CMU/STI module, while the corresponding yields in O/G are 4.0 and 17.7 g per mol of reactant. Therefore, the yields of condensable compounds from TOL and XYL reactions do not contribute to any significant extent to the discrepancy in the predictions of SOA between these models. Differences in partitioning characteristics that contribute to the discrepancies in SOA

predictions in these two models are discussed below in the Implementation of Gas/Particle Partitioning Theory subsection. The same monoterpene emissions are represented by different numbers of model compounds in CMU/STI and O/G. The monoterpene reactions in the CMU/STI module result in relatively low yields of condensable compounds as compared to the O/G approach. In CMU/STI, the sum of the stoichiometric coefficients of the condensable compounds (Table 3) is 0.075 mol of condensable products (MW 180) per mol of reacted terpene. The corresponding yield of condensable products is 13.5 g per mol of reactant. Most terpene species represented in the O/G model, including the more abundant species (such as R-pinene, β-pinene, limonene, and the sesquiterpenes), give significantly higher total mass yields of condensable compounds per mole of terpene (see Table 1). More biogenic SOA are formed in the O/G module than the CMU/STI module due to greater amounts of condensable products being formed despite a partitioning process that favors the particle phase in CMU/STI. Comparing the Formation of Condensable Products between Empirical and Explicit Models. Condensable products are formed as first-generation products in the reactions added to the CMB-IV mechanism used with the O/G and CMU/STI modules. In CACM, many of the anVOL. 37, NO. 16, 2003 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

9

3655

FIGURE 7. Temporal profiles of SOA as predicted by three modules in Nashville on July 16-18, 1995. thropogenic condensable products are formed after one or more generations of stable intermediates. Even so, CACM forms more condensable compounds and SOA from anthropogenic emissions than the empirical models, indicating that this mechanistic model has higher SOA production potential than the empirical models. CACM includes the same anthropogenic precursors as CMU/STI, but the mechanistic representation of both precursors and products is very different from the empirical models. Many more condensable compounds from anthropogenic precursors are modeled in CACM (>30) than in the empirical models (4). Several of these explicit condensable compounds are quite volatile, resulting in very high concentrations of condensable gases 3656

9


(Table 7), as discussed in the Implementation of Gas/Particle Partitioning Theory subsection. For biogenic SOA, the two terpene species in CACM are classified based on SOA yields. Many first generation products of BIOL and BIOH are condensable compounds in CACM. Therefore, the yields of biogenic SOA are less affected by the chemical kinetics of the intermediates. For example, BIOH + OH f RO227; the subsequent reactions of the peroxy radical RO227 with NO, HO2, and RO2 give 1 mol of AP8 (C10 nitrato alcohol, MW 215) or UR7 (C10 oxoaldehyde, MW 168). The corresponding reaction for terpinene (BIOH) gives 0.46 mol of first-generation condensable compounds (MW 136) in the O/G module. Since AEC also models second generation

TABLE 7. Summary of 24-h Average SOA (µg m-3) Partitions Predicted by Three Modules and the Absorption Module of AEC at Three Sites: Land between Lakes (LBL), Nashville (NAS), and Youth, Inc. (YOU) O/G

July 16, 1995 SOA condensable gases SOA as % of total condensables 3-day average SOA condensable gases SOA as % of total condensables

CMU/STI

LBL

NAS

YOU

LBL

NAS

YOU

0.20 1.05 16

0.23 1.30 15

0.19 1.19 14

0.14 0.44 24

0.15 0.62 20

0.14 0.67 17

0.22 1.25 15

0.25 1.48 15

0.22 1.37 14

0.15 0.53 22

0.16 0.66 20

0.15 0.68 18

AEC (hydrophobic SOA module only)

AEC

July 16, 1995 SOA condensable gases SOA as % of total condensables 3-day average SOA condensable gases SOA as % of total condensables

LBL

NAS

YOU

LBL

NAS

YOU

1.98 8.96 18

2.29 5.57 29

1.88 4.84 28

1.82 0.23 89

2.14 0.26 89

1.77 0.24 88

2.14 9.02 19

2.58 7.42 26

2.12 7.04 23

1.96 0.25 89

2.40 0.29 89

1.98 0.27 88

condensable products, the AEC module produces higher yields of biogenic SOA than both empirical approaches. Implementations of Gas/Particle Partitioning Theory. Different formulations of gas/particle partitioning are used in the three modules. The AEC module allows both hydrophobic and hydrophilic condensable compounds to partition to the organic phase when an aqueous phase is not present. Since, in this particular application to the Nashville region, predictions of hydrophilic SOA are associated primarily with nighttime periods of high relative humidity, the formation of hydrophilic SOA is dominated by dissolution rather than by absorption. Moreover, the average concentration of hydrophilic SOA was much lower than that of hydrophobic SOA (see Figure 8). Figure 6 shows the partition of hydrophobic condensable products at LBL and NAS. The hydrophobic SOA accounted for over 90% of the SOA predicted by the AEC module. Shown in Table 7 are the average amounts of condensable gases and SOA predicted by the absorption modules. The gas/ particle partitioning characteristics are remarkably similar for a given module at different sites, despite differences in the amounts of total condensable products. The hydrophobic absorption module in AEC resulted in the highest proportion of condensable products (∼90% by mass) in the particulate phase. Considering both hydrophobic and hydrophilic compounds, the AEC module favors the gas phase, indicating that hydrophilic compounds are more volatile than the hydrophobic compounds. The CMU/STI module partitioned about 18-22% of the total condensable mass into the particulate phase at urban and rural sites. The O/G module tended to favor partitioning into the gas phase, with only 14-15% of the condensable products forming SOA. The slight difference in the partitioning characteristics between the two empirical models stems from the partitioning parameters selected for terpenes and for one anthropogenic SOA species. The aromatic SOA compounds have very similar partitioning characteristics (within 15% at reference temperatures) in the CMU/STI and O/G modules.

As explained above, a SOA species is used in the CMU/STI module to represent the formation of SOA from alkanes, alkenes, and secondary aromatic compounds, which are not considered to form SOA in the O/G module. SOA4 plus its gas-phase equivalent accounts for about 18-19% of the anthropogenic condensable species. Since it also has lower vapor pressure than the aromatic SOA, the overall partitioning of anthropogenic SOA favors the particulate phase in the CMU/STI module relative to the O/G module. For terpenes, the CMU/STI module forms two SOA products with a fairly low saturation vapor pressure of 6.6 µg m-3 at experimental temperature. Of the 34 biogenic SOA compounds modeled in O/G, only 8 have lower vapor pressures. Therefore, the overall partitioning of biogenic SOA from the mixture of terpenes in the O/G module favors the gas phase when compared to the CMU/STI module. Both empirical modules employ the Clausius-Clapeyron equation for temperature correction using similar ∆Hvap values (72.7 kJ mol-1 in O/G and 70 kJ mol-1 in CMU/STI). While small differences in the partitioning parameters are noted above, a 3 K difference in the experimental temperature (Tables 2 and 4) assumed for the correction translates to a 20% difference in the correction factor at 295 K, the lowest surface temperature in the simulation. The effect of temperature correction on the formation of SOA will be discussed further under the Temperature Dependence of SOA Formation subsection. One key difference between the empirical SOA modules and the explicit module is the partitioning parameters. In the empirical modules, the partitioning parameters and saturation vapor pressures of condensable products are obtained from experimental data, and the Clausius-Clapeyron equation is used to correct for the difference between experimental and model temperatures. For the AEC module, the partitioning constants (eq 4) are determined based on the saturation vapor pressures obtained from property estimation methods. Property estimation methods were used because the thermodynamic properties of many of the complex organic compounds in the SOA mixture have not been determined in the laboratory. From the difference in the partitioning characteristics of the models, it can be concluded that a significant difference exists between the empirically determined and temperature corrected partitioning coefficients used in O/G and CMU/STI and the partitioning coefficients based on estimated properties used in AEC. Since property estimation methods tend to be developed for less polar compounds than those comprising SOA, these parameters may well be a very uncertain area for the models based on explicit SOA compounds. In a previous application of CMAQ to the Los Angeles basin, primary organic emissions were found to be underrepresented (38) due to the default composition assumed for PM2.5 emissions within CMAQ. According to eq 1, the presence of primary absorbing OC enhances the partitioning of SOA into the particulate phase. An underestimation of primary OC affects more significantly the partitioning of SOA in a situation where little SOA is formed, as the absorbing medium consists mostly of primary OC. For example, in a sensitivity simulation where primary OC is not included as part of the absorbing medium in the CMU/STI model, the amount of SOA formed is negligible. In the event where the concentration of SOA is larger as compared to primary OC, underestimation of primary OC has a smaller effect on the partitioning of SOA into a solution consisting mostly of SOA. Therefore, an underestimation of primary OC can magnify the discrepancy of the two empirical modules and the AEC module, which yields higher SOA concentrations. Formation of SOA by Dissolution. The availability of particulate water is a key variable for the dissolution of hydrophilic SOA. Figure 9 shows cross plots of the amount VOL. 37, NO. 16, 2003 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

9

3657

FIGURE 8. Hydrophilic (type A) and hydrophobic (type B) SOA predicted at three sites by the AEC SOA module of MADRID 2. of hydrophilic SOA versus particulate water and versus relative humidity (RH). (Since RH was not a direct input/ output for CMAQ, the values plotted in Figure 9 were calculated on the basis of temperature and mixing ratios of water vapor and could be slightly different from the RH values used in the model.) Note that condensed water is the sum of the water associated with aqueous inorganic particles and that associated with hydrophilic OC in the aqueous solution. As expected, concentrations of hydrophilic SOA correlate positively with condensed water and RH. The scatter in the correlation may be due to the formation of an organic solution by absorption rather than dissolution at low RH when no preexisting aqueous particles exist. In addition, one of the SOA surrogates undergoes deliquescence at 79% RH. The effect of deliquescence is small in this simulation partly because the surrogate compound that is subject to deli3658

9


quescence is relatively volatile, with a Henry’s law constant of 1.6 × 106 M/atm. A sensitivity simulation was carried out to study the effects of Henry’s law constant uncertainties on the predictions of SOA formation. Dissolution parameters (such as Henry’s law constants) for the surrogate species are uncertain because they are determined on the basis of the properties of individual compounds represented in the group. In an alternative formulation of the MADRID 2 model, the deliquescent surrogate compound was used to represent a group of individual species with a geometric average Henry’s law constant of 109 M/atm. As seen in Figure 10, significantly more hydrophilic SOA can be formed at LBL, especially at RH above ∼80%; then, a significant growth of hydrophilic SOA indicates the contribution of the deliquescent compound when its affinity for the aqueous phase is increased. These

FIGURE 9. Hydrophilic SOA vs particulate water (left-hand side) and RH (right-hand side) predicted by MADRID 2/AEC at LBL, NAS, and YOU. results point out the importance of correctly characterizing the partitioning properties (e.g., Henry’s law constant and dissociation constants) of hydrophilic condensable compounds. Temperature Dependence of SOA Formation. The use of the Clausius-Clapeyron equation to correct the saturation vapor pressure as a function of temperature requires that the enthalpy of vaporization (∆Hvap) be specified. ∆Hvap is a highly uncertain parameter that is the focus of recent work (e.g., refs 24 and 39-41). Figure 11 shows two sensitivity simulations conducted using the O/G module. For these simulations, changing the value of ∆Hvap changed the magnitude of SOA predictions with only small changes in the temporal patterns. In one case, no temperature dependence was simulated (i.e., ∆Hvap ) 0 kJ/mol); the formation of SOA is reduced by more than a factor of 2. In the other case, a value of 156 kJ/mol was used for ∆Hvap, following Strader et al. (12). SOA formation increases by a factor of about 2.5. CMU/STI and the hydrophobic SOA AEC module

also use saturation vapor pressure to characterize the partitioning coefficients and are subject to the same type of uncertainties relating to temperature effects. Because the hydrophobic AEC module uses parameters that were estimated at ambient temperatures, uncertainties in temperature effects are less important for this module than the empirical modules, which use experimental parameters obtained at higher temperatures. Nonetheless, for models that relate partitioning characteristics to saturation vapor pressure, proper representation of temperature effects is key in SOA predictions.

Implications Modeling SOA formation is among the most demanding of aspects associated with atmospheric organic photooxidation because the formation process depends on (a) the representation of those parent organic species the oxidation of which leads to condensable products, (b) the gas-phase chemistry that leads to the condensable species, and (c) the VOL. 37, NO. 16, 2003 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

9

3659

FIGURE 10. Time series of RH and hydrophilic SOA predicted by MADRID 2 at LBL. Significantly higher concentrations of hydrophilic SOA were simulated when the Henry’s law constant was increased for a surrogate in the sensitivity case.

FIGURE 11. Time series of SOA concentrations predicted at LBL using the O/G model. The base case uses a value of 73 kJ/mol for ∆Hvap in the Clausius-Clapeyron equation. The two sensitivity cases use 156 kJ/mol and no temperature dependence (K ) K310), respectively, for the partitioning coefficients. representation of the gas/particle partitioning process of the condensable compounds. Large uncertainties are associated with biogenic VOC emission inventories. The formation of biogenic SOA is sensitive to the representation of high yield versus low yield terpene species. Therefore, more detailed monoterpene representation is desirable both in emissions and air quality simulations. Further experimental work is needed to define the identities of the partitioning compounds as well as the parameters for the gas/particle partitioning of SOA. Current modules differ significantly in both areas. A large difference in SOA predictions is indicative of uncertainties in our current understanding of the chemical mechanisms of SOA formation, as compiled in CACM and that deduced from the available experimental data used in the formulations of the O/G and CMU/STI modules. There exist some differences in the two empirical modules despite their similar basis on experimental data. Evaluations of SOAforming mechanisms using smog chamber data should help refine the gas-phase chemical mechanisms. Sensitivity studies show that reducing the uncertainties associated with temperature dependence may be key to improving absorptive partitioning models, and a better understanding is also needed to bound uncertainties in the formation of water soluble SOA. In addition, in some applications, uncertainties 3660

9


in boundary conditions and emissions of other PM components (e.g., primary OC) have an effect on SOA predictions. Better measurements, including high time resolution OC measurements, will be useful for evaluating SOA modeling tools.

Acknowledgments Funding for this work was provided by the Coordinating Research Council (CRC) under Project A-23 and the American Chemistry Council under Contract 0105. The development of the CMU/STI module was sponsored by CRC. The AEC module was developed by AER under funding from the California Air Resources Board (CARB) and EPRI. Thanks are also due to EPRI for allowing the use of the CMAQ inputs of the Nashville/western Tennessee O3 simulation. Professor Helen Suh (Harvard University) provided PM and sulfate data from the MAACS study for comparison with model results.

Literature Cited (1) Odum, J. R.; Hoffmann, T.; Bowman, F.; Collins, D.; Flagan, R. C.; Seinfeld, J. H. Environ. Sci. Technol. 1996, 30, 2580. (2) 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.

(3) Hoffmann, T.; Odum, J. R.; Bowman, F.; Collins, D.; Klockow, D.; Flagan, R. C.; Seinfeld, J. H. J. Atmos. Chem. 1997, 26, 189. (4) Griffin, R. J.; Cocker, D. R., III; Flagan R. C.; Seinfeld, J. H. J. Geophys. Res. 1999, 104, 3555. (5) Yu, J.; Griffin, R. J.; Cocker, D. R., III; Flagan, R. C.; Seinfeld, J. H. Geophys. Res. Lett. 1999, 26, 1145. (6) Cocker, D. R., III; Clegg, S. L.; Flagan, R. C.; Seinfeld, J. H. Atmos. Environ. 2001, 35, 6049. (7) Cocker, D. R., III; Mader, B. M.; Kalberer, M.; Flagan, R. C.; Seinfeld, J. H. Atmos. Environ. 2001, 35, 6073. (8) Kamens, R. J.; Jaoui, M. Environ. Sci. Technol. 2001, 35, 1394. (9) Jang, M.; Czoschke, N. M.; Lee, S.; Kamens, R. M. Science 2002, 296, 814. (10) Ziemann, P. J. J. Phys. Chem. A 2002, 106, 4390. (11) Strader, R.; Gurciullo, C.; Pandis, S.; Kumar N.; Lurmann, F. W. Final Report STI-997510-1822; Coordinating Research Council: Alpharetta, GA, 1998; NTIS PB99-128738. (12) Strader, R.; Lurmann, F.; Pandis, S. N. Atmos. Environ. 1999, 33, 4849. (13) Barthelmie R. J.; Pryor, S. C. J. Geophys. Res. 1999, 35, 23657323669. (14) Aumont, B.; Madronich, S.; Bey, I.; Tyndall, G. S. J. Atmos. Chem., 2000, 35, 59. (15) Andersson-Sko¨ld, Y.; Simpson, D. J. Geophys. Res. 2001, 106, 7357. (16) Schell, B.; Ackermann, I. J.; Hass, H.; Binkowski, F. S.; Ebel, A. J. Geophys. Res. 2001, 106, 28275. (17) Pun, B. K.; Griffin, R.; Seigneur, C.; Seinfeld, J. H. J. Geophys. Res. 2002, 107 (D17), 2001D000542. (18) Griffin, R. J.; Dabdub, D.; Kleeman, M. J.; Fraser, M. P.; Cass, G. R.; Seinfeld, J. H. J. Geophys. Res. 2002, 107 (D17), 2001JD000544. (19) Griffin, R. J.; Nguyen, K.; Dabdub, D.; Seinfeld, J. H. J. Atmos. Chem. 2003, 44, 171. (20) Science Algorithms of the EPA Models-3 Community Multiscale Air Quality (CMAQ) Modeling System; U.S. Environmental Protection Agency: Research Triangle Park, NC, 1999; EPA/ 600/R-99/030. (21) Lamb, B.; Grosjean, D.; Pun B.; Seigneur, C. Final Report A-23; Coordinating Research Council: Alpharetta, GA, 1999; NTIS PB2000-192875. (22) Pankow, J. F. Atmos. Environ. 1994, 28, 189. (23) Tao, Y.; McMurry, P. H. Environ. Sci. Technol. 1989, 23, 1519.

(24) Bilde, M.; Pandis, S. N. Environ. Sci. Technol. 2001, 35, 3344. (25) Gery, M. W.; Whitten, G. Z.; Killus, J. P. Report; U.S. EPA: Research Triangle Park, NC, 1988; EPA/600/3-88/012. (26) Griffin, R. J.; Dabdub, D.; Seinfeld, J. H. J. Geophys. Res. 2002, 107 (D17), 2001JD000541. (27) Organic Aerosol Partition Module Documentation; EPRI: Palo Alto, CA, 1999; TR-113095. (28) Fredenslund, A.; Gmehling, J.; Rasmussen, P. Vapor-Liquid Equilibrium Using UNIFAC; Elsevier: Amsterdam, 1977. (29) Nenes, A.; Pandis, S.; Pilinis, C. Atmos. Environ. 1999, 33, 1553. (30) Stokes, R. H.; Robinson, R. A. J. Phys. Chem. 1966, 70, 2126. (31) Pun, B. K.; Zhang, Y.; Vijayaraghavan, K.; Wu, S.-Y.; Seigneur, C.; Seinfeld, J. H. Proceedings, A&WMA Specialty Conference on Regional Haze and Global Radiation Balance, Bend, OR, 2001. (32) Seigneur, C.; Pun, B. K.; Zhang, Y.; Vijayaraghavan, K.; Wu, S.-Y.; Kumar, N. Air Quality III; Arlington, VA, 2002. (33) Karamchandani, P.; Seigneur, C.; Lohman, K.; Pai, P.; Zhang, Y.; Vijararaghavan, K.; Sykes, I.; Santos, L.; Nehrkorn, T.; Henderson, J.; Louis, J. F.; Hanson, A. Presented at the 92nd Air and Waste Management Association Annual Meeting, St. Louis, MO, 1999. (34) Pun, B. K.; Wu, S.-Y.; Seigneur, C. Environ. Sci. Technol. 2002, 36, 3586. (35) Geron, C.; Guenther, A.; Pierce, T. J. Geophys. Res. 1994, 99, 12773. (36) Helmig, D.; Klinger, L.; Guenther, A.; Vierling, L.; Zimmerman, P.; Geron, C. Chemosphere 1999, 38, 2163. (37) Suh, H. H.; Nishioka, Y.; Allen, G. A.; Koutrakis, P.; Burton, R. M. Environ. Health Perspect. 1997, 105, 826. (38) Seigneur, C.; Karamchandani, P.; Pai, P.; Vijayaraghavan, K.; Lohman, K.; Wu, S. Y. Presented at the First Annual Models-3 Workshop, Arlington, VA, 2000. (39) Turner, F. M.S. Thesis, Carnegie Mellon University, Pittsburgh, PA, 1998. (40) Aw, J.; Kleeman, M. J. Presented at the 20th Annual AAAR Conference, Portland, OR, 2001. (41) Sheehan, P. E.; Bowman, F. M. Environ. Sci. Technol. 2001, 35, 2129.

Received for review February 20, 2003. Revised manuscript received May 27, 2003. Accepted June 3, 2003. ES0341541

VOL. 37, NO. 16, 2003 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

9

3661

Uncertainties in Modeling Secondary Organic Aerosols: Three

Recommend Documents