Model Representation of Secondary

Organic Aerosol in CMAQv4.7

ANNMARIE G. CARLTON,* , †

PRAKASH V. BHAVE,*

SERGEY L. NAPELENOK,

EDWARD O. EDNEY, GOLAM SARWAR,

ROBERT W. PINDER,

GEORGE A. POULIOT, AND

MARC HOUYOUX

U.S. Environmental Protection Agency, Research Triangle

Park, North Carolina 27711

Received February 26, 2010. Revised manuscript received

July 22, 2010. Accepted July 29, 2010.

Numerous scientific upgrades to the representation of

secondary organic aerosol (SOA) are incorporated into the

Community Multiscale Air Quality (CMAQ) modeling system.

Additions include several recently identified SOA precursors:

benzene, isoprene, and sesquiterpenes; and pathways: in-cloud

oxidation of glyoxal and methylglyoxal, particle-phase

oligomerization, and acid enhancement of isoprene SOA. NOxdependent aromatic SOA yields are also added along with

new empirical measurements of the enthalpies of vaporization

and organic mass-to-carbon ratios. For the first time, these

SOA precursors, pathways and empirical parameters are included

simultaneously in an air quality model for an annual simulation

spanning the continental U.S. Comparisons of CMAQmodeled secondary organic carbon (OCsec) with semiempirical

estimates screened from 165 routine monitoring sites across

the U.S. indicate the new SOA module substantially improves

model performance. The most notable improvement occurs inthe

central and southeastern U.S. where the regionally averaged

temporal correlations (r) between modeled and semiempirical

OCsec increase from -0.5 to 0.8 and -0.3 to 0.8, respectively,

whenthe new SOA module is employed.Wintertime OCsec results

improve in all regions of the continental U.S. and the seasonal

and regional patterns of biogenic SOA are better represented.

Introduction

The simplistic treatment of secondary organic aerosol (SOA)

is a source of considerable uncertainty in air quality model

predictions (1). SOA typically forms in the atmosphere when

organic gases react with oxidants (e.g., O3, ·OH, ·NO3) and

form low-volatility products that can be absorbed into the

organic fraction of particulate matter (PM). In the U.S.,

secondary formation typically accounts for 50% of particulate

organic carbon (OC) in summer and 20% in winter (2).

Regional air quality models consistently underestimate

ground-level fine PM in the summer at both urban and rural

locations, primarily a result of errors in organic PM (3-7).

Conversely, wintertime OC is often overestimated (8, 9) due,

in part, to SOA formulations that employ overestimated

enthalpy of vaporization (∆Hvap) values (10). In addition,

models predict little to no anthropogenic SOA (11, 12) though

it is observed in the atmosphere (e.g., refs 13, 14). These

biases persist even when detailed SOA mechanisms and

partitioning schemes are employed (15), suggesting that the

current SOA treatment typically used in atmospheric models

is incomplete and requires revision (16).

New findings indicate that several atmospherically important SOA precursors and formation pathways are not

widely incorporated in air quality models. For example,

isoprene, sesquiterpenes, and benzene have recently been

recognized as SOA precursors (17-19) that contribute

substantially to ambient organic PM (20-24). There is

increasing evidence that SOA formation is influenced by the

abundance of NOx (18, 25-29), the acidity of seed particles

(30-32), and the extent of atmospheric aging (33-35). In

addition to gas-phase oxidation of organic compounds, it is

becoming well-established that SOA can form through

aqueous oxidation processes (36-38). Finally, mounting

evidence from laboratory and field measurements indicates

that atmospheric SOA exhibits very low volatility (e.g., LVOOA) (35), yet atmospheric models typically treat all SOA as

semivolatile compounds that partition reversibly between

the gas and particulate phases.

CMAQ is among the most widely used air quality models,

with 2000+ registered users in 90 different countries

(www.cmaq-model.org). The U.S. Environmental Protection

Agency, as well as state and local governments, use CMAQ

for regulatory applications (e.g., ref 39) and the National

Oceanic and Atmospheric Administration employs CMAQ

for routine air quality forecasting (40). Given the widespread

use of CMAQ, it is important that its algorithms reflect

scientific knowledge that is accepted across the research

community. The last major update to the SOA treatment in

a public version of CMAQ was in 2003 as part of version 4.3.

Since then, researchers have used the CMAQ modeling

framework to explore the importance of various SOA formation pathways and parametrizations (e.g., refs 1, 11, 15, 24,

41, 42). Meanwhile, a number of SOA formation mechanisms

proposed by other researchers were assessed in EPA’s smog

chamber facilities (e.g., refs 21, 31, 32, 43-45), and a tracerbased method for estimating the contributions of different

sources to ambient SOA was developed (14).

The purpose of this paper is to describe updates to the

SOA treatment in version 4.7 of the CMAQ model (CMAQv4.7)

and document the impact of those updates on a year-long

model simulation spanning the continental United States.

Principal objectives of CMAQv4.7 are to simulate all major

SOA formation processes that are supported by peer-reviewed

laboratory studies and all major sources of SOA that have

been identified in field samples, without adding substantially

to the computational burden of CMAQv4.6. Given the rate

at which new knowledge about SOA is being generated in

the literature, it is not possible for models to keep pace with

every new finding. The SOA treatment described in the

present study reflects the state of scientific knowledge as of

August 2007. After extensive testing, the new model formulation was released to the public in December 2008 as part

of CMAQv4.7. Other major improvements to CMAQv4.7

include an updated N2O5 hydrolysis parametrization, dynamic mass transfer for coarse-mode aerosol, revisions to

the cloud model and new photolysis modeling options (7).

Model Description

The SOA treatment in CMAQv4.3 through v4.6 has been

described elsewhere (Supporting Information (SI) Section

* Address correspondence to either author. carlton@envsci.

rutgers.edu (A.G.C.); bhave.prakash@epa.gov (P.V.B.). † Current address: Department of Environmental Sciences, 14

College Farm Road, New Brunswick, NJ 08903.

Environ. Sci. Technol. 2010, 44, 8553–8560

10.1021/es100636q  2010 American Chemical Society VOL. 44, NO. 22, 2010 / ENVIRONMENTAL SCIENCE & TECHNOLOGY 9 8553

Published on Web 09/30/2010
S-1, Figure S-1). In those model versions, SOA was formed

exclusively via gas-phase oxidation of monoterpenes, alkylsubstituted single-ring aromatics, cresol, and long alkanes,

by free radicals (·OH, ·NO3, O(3

P)) and O3. The semivolatile

products of these oxidation reactions were subsequently

absorbed into an organic particulate phase (46, 47) and their

gas/particle partitioning behavior was parametrized with a

strong temperature dependence (∆Hvap ) 156 kJ mol-1

). A

detailed description of the SOA treatment in previous CMAQ

versions is provided in SI Section S-1, along with the governing

equations for modeling semivolatile partitioning.

In CMAQv4.7, SOA is produced from an expanded list of

precursors (Table 1) and can be formed via both gas- and

aqueous-phase oxidation processes, as well as aerosol-phase

reactions. Seven of the 19 secondary organic PM species are

treated as nonvolatile (dark-shaded rectangles in Figure 1).

The 12 remaining species are semivolatile and have ∆Hvap

values reduced in accordance with recent experimental data

(48). Effective saturation concentrations (c*) at 298 K are

derived from laboratory data using the new ∆Hvap values and

assuming a representative chamber temperature (Table 1

footnote g).

Semivolatile SOA. In CMAQv4.7, seven organic species

(shown along the left edge of Figure 1) may be oxidized

in the gas phase to form semivolatile products. The

stoichiometric yields (R) and partitioning parameters (c*,

∆Hvap) of these products are provided in Table 1. Whereas

previous versions of CMAQ treated monoterpenes as the

only biogenic precursor of SOA, CMAQv4.7 includes SOA

formation from isoprene, monoterpenes, and sesquiterpenes. Isoprene SOA is formed exclusively via oxidation

by · OH. Its yield and partitioning behavior are modeled

according to the two-product parametrization of Henze

and Seinfeld (22), based on the low-NOx laboratory

experiments of Kroll et al. (18). As in previous versions of

CMAQ, SOA parameters for monoterpenes are based on

the daylight experiments of Griffin et al. (49). Yields and

partitioning parameters for five different monoterpenes

are lumped together using U.S. emissions-based weighting

factors (see SI Table S-3) and the methodology of Bian and

Bowman (50). In CMAQv4.7, the mass-based yields (RTRP1,

RTRP2) and partitioning parameters (c*TRP1, c*TRP2) are

corrected to replace the original assumption of unit density

(49) with more recent experimental data (SI Section S-1).

SOA from sesquiterpenes is parametrized in an analogous

fashion (i.e., experimental data of Griffin et al. (49),

emissions-based lumping (50), and density correction),

but only one semivolatile product (SV_SQT) is required to

fit the laboratory data (49). One of the most consequential

updates in CMAQv4.7 is the reduction of ∆Hvap from 156

kJ mol-1 to 40 kJ mol-1 for all biogenic SOA species, based

on recent chamber measurements of R-pinene SOA (48).

TABLE 1. Parameters in CMAQv4.7 SOA Module

gaseous precursor

(model species) oxidants

semivolatile

products rj

f

c* g

(μg m-3)

∆Hvap

(kJ mol-1)

SOA

species

M

(g mol-1)

OM/OC

ratio

Semivolatile SOA

isoprene (ISOPa or ISOPRENEb

) ·OH SV_ISO1 0.232 116.01 40 AISO1 96 1.6

SV_ISO2 0.0288 0.617 40 AISO2 96 1.6

monoterpenes (TERPa or TRP1b

) ·OH, O(3P)e,

O3, ·NO3

SV_TRP1 0.1393 14.792 40 ATRP1 168 1.4

SV_TRP2 0.4542 133.7297 40 ATRP2 168 1.4

sesquiterpenes (SESQa,b

) O3, ·OH,

·NO3 SV_SQT 1.537 24.984 40 ASQT 378 2.1

long alkanes (ALK5b

) ·OH SV_ALK 0.0718 0.020 40 AALK 150 1.56

high-yield aromatics (TOLa or ARO1b

) SV_TOL1 0.0758 2.326 18 ATOL1 168 2.0

·OH/NO SV_TOL2 0.1477 21.277 18 ATOL2 168 2.0

low-yield aromatics (XYLa or ARO2b

) SV_XYL1 0.0386 1.314 32 AXYL1 192 2.0

·OH/NO SV_XYL2 0.1119 34.483 32 AXYL2 192 2.0

benzene (BENZENEa,b

) SV_BNZ1 0.0942 0.302 18 ABNZ1 144 2.0

·OH/NO SV_BNZ2 1.162 111.11 18 ABNZ2 144 2.0

Nonvolatile SOA

high-yield aromaticsc ·OH/ · HO2 0.471 h h ATOL3 168 2.0

low-yield aromaticsc ·OH/ · HO2 0.373 h h AXYL3 192 2.0

benzenec ·OH/ · HO2 0.484 h h ABNZ3 144 2.0

isoprene H+ h h AISO3 162 2.7

aged aerosol time h h AOLGA 176.4 2.1

AOLGB 252 2.1

glyoxal, methylglyoxal (GLYa

, MGLYa,b,d) ·OH 0.04 h h AORGC 177 2.0

a Indicates species name in CB05 gas-phase mechanism. Long alkanes and glyoxal are not modeled explicitly in CB05. b Indicates name in SAPRC99. c Low-NOx pathways. Mole-based R values reported by Ng et al. (28) are divided by the

stoichiometric coefficients for peroxy radical products (taken from the SAPRC99 gas-phase chemical mechanism) to obtain

the values tabulated here. d When CMAQ is exercised with the CB05 mechanism, the Henry’s Law constant for

methylglyoxal is adjusted in the aqueous chemistry routine to the glyoxal value (because GLY is not an explicit species in

CB05) to ensure that simulations employing SAPRC99 and CB05 provide similar predictions of AORGC at similar aqueous

phase aldehyde concentrations. e There is no O(3P) species in the CB05 gas-phase mechanism. When CB05 is employed,

TRP1 may react with singlet O to form SOA. f Rj are mass-based stoichiometric yields. In the aqueous-phase production of

AORGC, 0.04 is a molar-based yield. g c* values are tabulated at 298 K, using the ∆Hvap values shown and assuming a

representative chamber temperature (313 K for monoterpenes, 310 K for sesquiterpenes, 281.5 K for long alkanes, and no

correction for the remaining experiments which were conducted between 293 and 298 K). h For nonvolatile SOA, c* and

∆Hvap values are not required in atmospheric models.

8554 9 ENVIRONMENTAL SCIENCE & TECHNOLOGY / VOL. 44, NO. 22, 2010
The organic mass-to-carbon ratios (OM/OC) in Table 1

are obtained from laboratory measurements (14) and used

to estimate the molar masses (M) of each semivolatile

product for the absorptive partitioning calculations.

The formation of semivolatile SOA from anthropogenic

organic gases is depicted in the upper-left portion of Figure

1 and parameters are given in Table 1. Like previous versions

of CMAQ, long alkanes (∼8 carbon atoms) oxidize to yield

one semivolatile product (SV_ALK) that is parametrized by

Strader et al. (51) based on early measurements of Grosjean

and Seinfeld (52). Due to the lack of experimental data at the

time of this model update, we arbitrarily assign SV_ALK the

same ∆Hvap as our biogenic oxidation products (i.e., 40 kJ

mol-1

) and a molar mass of 150 g mol-1. In previous CMAQ

model versions, SOA formation from aromatic compounds

was parametrized based on the laboratory data of Odum et

al. (53) by assuming that a single reaction with the ·OH radical

would produce semivolatile products. In CMAQv4.7, R and

c* values for the semivolatile aromatic reaction products are

taken from newer laboratory data (28) and these products

are formed via a two-step process: initial reaction with ·OH

produces aromatic peroxy radicals and subsequent reaction

of the peroxy radicals with NO yields semivolatile organic

nitrates (23). This treatment is applied to high-yield aromatics

(based on laboratory data for toluene), low-yield aromatics

(based on m-xylene data), and benzene. The OM/OC ratio

of 2.0 for all six semivolatile aromatic reaction products is

taken from laboratory measurements of toluene SOA (14).

For the low-yield aromatic products (SV_XYL1 and SV_XYL2),

∆Hvap is taken from laboratory irradiations of m-xylene and

1,3,5-trimethylbenzene (J.H. Offenberg, personal communication). The ∆Hvap value for SV_TOL1 and SV_TOL2 (18 kJ

mol-1

) is from the upper end of the range for toluene

(13.8-17.5 kJ mol-1

) (48) and is also applied to the benzene

products (SV_BNZ1 and SV_BNZ2). Our final update to the

semivolatile SOA treatment in CMAQv4.7 is the removal of

cresol as an explicit SOA precursor. Any SOA formed from

cresol is likely counted in the toluene SOA parametrization

described above because cresol is a product of toluene

oxidation.

Nonvolatile SOA. A major enhancement to the SOA

treatment in CMAQv4.7 is the inclusion of nonvolatile SOA

species, which are depicted as dark-shaded rectangles in

Figure 1. Unlike the semivolatile SOA described above,

these species do not partition back to the gas phase. Four

types of nonvolatile SOA are included in CMAQv4.7 (see

bottom of Table 1): (1) low-NOx aromatic SOA, (2) acidenhanced isoprene SOA, (3) oligomers formed through

particle-phase reactions, and (4) SOA from aqueous-phase

oxidation.

Under low-NOx conditions, aromatic peroxy radicals

react preferentially with · HO2 (instead of NO) to produce

nonvolatile SOA (28). Although ground-level · HO2 mixing

ratios in CMAQ are 1-2 orders of magnitude smaller than

NO across the remote continental U.S. and over 1000 times

smaller than NO in urban areas, the low · HO2 abundance

is slightly compensated by its fast reaction rate with

aromatic peroxy radicals (50% faster than the rate of · RO2

+ NO at 35 °C and 150% faster at -10 °C; see SI Table S-4).

Moreover, the stoichiometric yields of nonvolatile products

are substantially larger than those of the semivolatile

products (see Table 1). Combining the faster reaction rates

and larger yields with the fact that the ·RO2 + ·HO2 reaction

products reside entirely within the particle phase, we expect

FIGURE 1. Schematic of CMAQv4.7 SOA module. Species inside the circle constitute the organic fine PM. Species in the dark

shaded boxes are treated as nonvolatile. Gas-phase oxidation pathways are depicted on the left and aqueous-phase oxidation is

shown on the right. Double-headed arrows spanning the gas/particle interface represent the equilibrium partitioning of semivolatile

SOA species.

VOL. 44, NO. 22, 2010 / ENVIRONMENTAL SCIENCE & TECHNOLOGY 9 8555
nonvolatile aromatic SOA to rival the abundance of the

semivolatile species described above (ATOL1, ATOL2,

AXYL1, etc.) (23).

Although several laboratory studies have demonstrated

that organic PM production from isoprene is enhanced

relative to pH-neutral conditions (21, 30, 31), only Surratt

and co-workers include a parametrization of the impact

of acidity. An enhancement factor, kacid ) 0.00361 m3

nmol-1, is calculated from Figure 1 of reference (31) by

normalizing the laboratory-derived slope (0.0389 μgC

nmol-1) by the intercept term (10.733 μgC m-3) shown in

that figure. Acidity is parametrized using the particle-phase

hydrogen ion concentration in air (H+ nmol m-3 air), which

we estimate by subtracting the molar concentration of

fine-particle cations (ammonium and sodium) from the

equivalent concentration of anions (nitrate, chloride and

twice the sulfate) and we constrain H+ within the range

of experimental data (0 e [H+] e 530 nmol m-3) (31). The

additional secondary organic carbon OCsec due to particle

acidity is treated as a nonvolatile species in CMAQ and is

calculated by multiplying kacid by the isoprene (OCsec)

formed via semivolatile partitioning, with appropriate

measures to ensure mass conservation.

All semivolatile organic PM species in the model undergo

condensed-phase oligomerization reactions that produce

nonvolatile products by a first order rate, kolig ) 9.6 × 10-6

s-1, corresponding to a 20 h half-life (11, 33). The oligomers

originating from biogenic gases (AOLGB) are tracked separately from those originating from anthropogenic gases

(AOLGA). The OM/OC ratio for oligomers (2.1) is based on

a recommended value for nonurban areas (54).

The final nonvolatile species in CMAQv4.7 (AORGC) forms

from aqueous-phase oxidation of glyoxal and methylglyoxal

by the hydroxyl radical (·OH). These constituents partition

into cloud and fogwater according to Henry’s Law and lowvolatility reaction products remain in the particle phase after

the droplets evaporate. This treatment is depicted on the

right side of Figure 1 and has been described in detail in a

separate publication (42). The OM/OC ratio (2.0) is estimated

from laboratory experiments (55).

Gas-Phase Chemistry. CMAQv4.7 is fully compatible

with two chemical mechanisms: a recently updated Carbon

Bond mechanism (CB05) (56) and the 1999 Statewide Air

Pollution Research Center mechanism (SAPRC99) (57). To

each mechanism, we add 10 reactions and modify several

existing reactions to accommodate the new SOA treatment

in CMAQv4.7 (see SI Tables S-4 and S-5). Neither mechanism contains a sesquiterpene species, so three of our

new reactions simulate the oxidation of sesquiterpenes by

O3, · OH, and · NO3. Due to the large uncertainties in

sesquiterpene emissions and in the rates and product

distribution of the various reactions that may follow the

initial oxidation, we incorporate only their first-generation

decay in a manner that does not consume oxidants (e.g.,

SESQ + O3 f SV_SQT + O3) or affect CMAQ predictions

of O3 and other gases. Neither CB05 nor SAPRC99 treat

benzene explicitly, so we add three reactions to simulate

the oxidation of benzene by · OH and subsequent reactions

of the benzene peroxy radical with NO and · HO2. Finally,

we add four reactions to simulate the interactions of highand low-yield aromatic peroxy radicals with NO and · HO2.

See SI Section S-2 for a complete description of these

mechanistic changes.

Deposition. Dry and wet deposition of all 19 particlephase SOA species mimic the treatment applied to inorganic

PM species in previous CMAQ versions (58, 59). Note that

deposition is the only removal mechanism for nonvolatile

SOA. For the semivolatile organic gases (i.e., SV species in

Figure 1), wet deposition is simulated using adipic acid as

a surrogate and dry deposition velocities are copied from

acetic acid. Critical properties for these processes are Henry’s

Law constant, molecular weight, diffusivity and relative

reactivity.

Methodology

Model Simulations. Two CMAQ simulations of the 2003

calendar year are conducted on a 36 km grid that spans the

continental U.S. from the Earth’s surface to a 100mb altitude

(112 rows×148 columns×14 layers)232 064 computational

cells). Both simulations are driven by identical weather fields

from the MM5 meteorological model (60). The first simulation

uses the publicly available CMAQv4.6 source code with

SAPRC99 chemical mechanism and Rosenbrock solver, plus

some minor code changes. The second simulation, referred

to hereafter as CMAQv4.6s, uses the same code except the

SOA treatment is copied from CMAQv4.7. Source code for

the latter simulation is available from the authors upon

request. We purposely do not apply the full CMAQv4.7 model

for the second simulation because v4.7 contains several

scientific updates unrelated to SOA (7) and our objective is

to isolate only the impacts of our new SOA treatment on

CMAQ model performance. In addition to the two annual

simulations, three 21-day summertime simulations are

conducted with each model version (v4.6 and v4.6s) to

examine the sensitivity of modeled SOA to anthropogenic

emissions of nitrogen oxides (NOx), volatile organic compounds (VOC), and primary OC (OCpri).

Boundary Conditions. For the CMAQv4.6s simulations,

concentrations of anthropogenic SOA species at the

domain boundaries are obtained directly from the global

modeling results of Henze et al. (23) and biogenic SOA

species are obtained from Chung and Seinfeld (61) and

Liao et al. (5). Boundary values for SOA species that were

not explicitly treated in those studies (e.g., AALK, ASQT,

AOLGA, AOLGB) are scaled from the available explicit

values using ratios observed in CMAQ simulations near

the borders of the continental U.S. modeling domain.

Temperature-corrected concentrations of semivolatile

gases are calculated at the boundaries as a fractional ratio

of c* and the POC concentration is approximated as 0.15

μg m-3 (61). The vertical distribution of species, for which

additional information was not available, is scaled (exponential decay) from surface-level concentrations (62).

See SI Section S-3 for a complete description of the revised

CMAQ boundary conditions.

Emissions. For all simulations, anthropogenic emissions

are taken from the EPA’s 2002 National Emissions Inventory

(NEI). Emissions of biogenic species are computed using

the Biogenic Emission Inventory System (BEIS), with

updates to incorporate sesquiterpene emissions. Sesquiterpenes are lumped into a single compound class using

available measurements for R-farnasene, -caryophyllene,

and various other sesquiterpenes. Basal emission rates

(BER) for sesquiterpenes are derived from the EF-S06

emission case in Table 1 of reference 63. Dividing each

emission factor by the corresponding biomass density from

that study, we obtain BER of 0.175, 0.1084, 0.0552, and

0.2036 μg gdry-1 hr-1, for broadleaf trees, needle-leaf trees,

shrubs and bushes, and grasses and crops, respectively.

Each of the 230 tree species in BEIS is mapped to one of

these BER except Loblolly pine, for which 0.3 μg gdry-1 hr-1

is used (64). All BER are modulated based on the 2 m air

temperature (T, K) in each grid cell,

to obtain a temporally- and grid-specific emission rate

(ER). The factor of 0.17 is based on outdoor measurements

(65).

ER ) BER*e0.17(T-303) (1)

8556 9 ENVIRONMENTAL SCIENCE & TECHNOLOGY / VOL. 44, NO. 22, 2010
Model Evaluation. CMAQ model performance is evaluated against semiempirical estimates of OCsec by the procedure of Yu et al. (2).

The obs subscript indicates concentrations observed at

Interagency Monitoring of Protected Visual Environments

(IMPROVE) (http://vista.cira.colostate.edu/improve) and

Southeastern Aerosol Research and Characterization

(SEARCH) (http://www.atmospheric-research.com/public/)

network sites. (OC/EC)pri is a ratio calculated from CMAQ

outputs of OCpri and EC, reflecting the mixture of primary

sources impacting a given site at the time when each sample

was collected. Model-predicted ratios are assessed at all 165

monitoring sites following the procedure described by Yu et

al. (2), and 97 sites are selected for the subsequent model

evaluation of OCsec (Figure S2).

In CMAQv4.6, OCsec is calculated from eq 1 of reference

(9). In CMAQv4.6s, it is the sum of all 19 SOA species divided

by the OM/OC ratios listed in Table 1. Hourly OCsec

concentrations from each annual simulation are paired in

space with the semiempirical estimates at 97 sites and

temporally averaged to match the 24 h sampling schedule

of each network.

Results and Discussion

The diversity of SOA species in CMAQv4.6s is substantially

more complex than in previous model versions, allowing for

assignment of SOA mass to individual VOC precursors. With

CMAQv4.6, SOA derived from monoterpenes comprises most

of the predicted OCsec (Figure 2) and anthropogenic precursors contribute a small amount to a single anthropogenic

SOA species. With CMAQv4.6s, biogenic SOA still dominates

OCsec predictions, but there are six species that can be

assigned to three VOC precursors and two different formation

processes. Similarly, in v4.6s, there are ten anthropogenic

SOA species traceable to four VOC precursors and predictions

increase as a result of the additional species and formation

pathways (7) (SI Figure S-3).

The seasonal pattern for biogenic SOA mass predictions

improves with the new SOA mechanism (7), (SI Figure S-4).

However, the magnitude of OCsec decreases (compare Figures

2a and 2b) despite the addition of two biogenic precursors

(isoprene and sesquiterpenes) and two additional formation

pathways (oligomerization and acidic enhancement). This

result largely arises from changing ∆Hvap for biogenic SOA

from 156 kJ mol-1 in CMAQv4.6 to 40 kJ mol-1 in v4.6s. The

change to a lower∆Hvap value causes less semivolatile material

to partition into the particle phase at typical ambient

temperatures and, in general, less biogenic SOA is predicted

as a consequence. This very large effect is evidenced by

comparing the v4.6 biogenic OCsec (gray in Figure 2a) (which

originates entirely from monoterpenes) with OCsec from

monoterpenes in v4.6s (light green in Figure 2b).

Comparison with Semiempirical OCsec Estimates.

CMAQv4.6s simulations demonstrate better agreement

with semiempirical OCsec estimates than v4.6 (Figure 2).

FIGURE 2. Seasonal profiles for CMAQ-predicted OCsec

(CMAQv4.6 on top, CMAQv4.6s on bottom) and semiempirical

estimates (•), averaged by month across the 97 sites shown in

SI Figure S-2.

FIGURE 3. Sensitivity of SOA originating from anthropogenic VOC (a), biogenic VOC (b) and all pathways (c) to emission controls in

summertime CMAQv4.6 and CMAQv4.6s simulations. Percent reductions are calculated from 21-day simulations using surface-level,

domain-wide averages in which controllable emissions of VOCs, NOx, and OCpri are separately removed from the emission inputs.

Note: SOA derived from glyoxal and methylglyoxal via cloud processing is counted in (c), but excluded from (a) and (b).

OCsec ) OCobs - [min(OCobs,(

OC

EC)pri × ECobs)] (2)

VOL. 44, NO. 22, 2010 / ENVIRONMENTAL SCIENCE & TECHNOLOGY 9 8557
When averaged by month across 97 sites, temporal

correlation (r) between the modeled and semiempirical

OCsec concentrations improves from 0.54 in CMAQv4.6 to

0.86 using CMAQv4.6s. This correlation also improves in

individual regions of the U.S., most notably in the central

states and Southeast, where CMAQv4.6 exhibits a negative

correlation with the semiempirical estimates (-0.5 and

-0.3, respectively) and v4.6s exhibits a large positive

correlation (0.8 and 0.8) (SI Figures S-5 and S-6). In addition

to the improved correlation, we note that v4.6s exhibits a

slope closer to unity (slopev4.6s ) 0.31, slopev4.6 ) 0.13) and

an intercept closer to zero (interceptv4.6s ) 0.02, interceptv4.6

) 0.42).

The improved spatial and temporal trends in biogenic

SOA, which dominates predicted OCsec concentrations in

both simulations (Figure 2), drives the enhanced model

performance. In contrast to CMAQv4.6, the revised SOA

treatment (v4.6s) produces summertime maxima for OCsec

in all regions, consistent with the observational estimates

(compare SI Figures S-5 and S-6). In addition, the

wintertime (December-March) overestimates of OCsec that

were evident in CMAQ v4.6 are eliminated by the new SOA

treatment (Figure 2).

Emissions Sensitivities. Our updated SOA treatment also

affects CMAQ predictions of how ambient SOA might respond

to future NOx, VOC, and OCpri emission controls. To evaluate

these effects, three 21-day simulations (August 15 to September 4, 2003) are conducted with v4.6 and v4.6s in which

all controllable emissions of NOx, VOC or OCpri are removed.

Relative to v4.6, CMAQv4.6s predictions of SOA from anthropogenic VOC exhibit diminished sensitivity to NOx and

OCpri (Figure 3a). Note that in this context anthropogenic

VOC refers to alkanes and aromatic compounds, for which

some uncontrollable sources (e.g., wildfires) exist (66). The

diminished sensitivities in v4.6s arise from low-NOx aromatic

oxidation pathways that produce nonvolatile SOA with high

stoichiometric yield, and particle-phase oligomerization

pathways that are independent of OCpri concentrations. In

contrast, v4.6s predictions of SOA from biogenic VOC exhibit

heightened sensitivity to OCpri emissions (Figure 3b). This

increased sensitivity arises from the v4.6s addition of highly

reactive sesquiterpenes, whose SOA-forming potential is

limited only by the amount of available absorbing organic

mass (66). Total SOA predictions in v4.6s also exhibit

heightened sensitivity to VOC emission controls (Figure 3c)

due to the larger contribution of anthropogenic VOCs to the

total SOA mass.

Future Model Development. The SOA mechanism in

CMAQ includes several new precursors and formation

pathways, reflecting the state of scientific knowledge as of

August 2007. Predicted trends and patterns for OCsec are better

with the new mechanism, especially for biogenic products.

However, a substantial negative bias in summertime OCsec

persists, suggesting that uncharacterized SOA sources exist

and/or current modeling approaches are insufficient (e.g.,

ref 35). Future model upgrades may require the addition of

more SOA precursors and pathways (67), and will be guided

by diagnostic model evaluations against organic tracer data

(14).

Acknowledgments

This work is the result of a large collaboration across the

EPA involving a variety of groups, namely AMAD, HEASD,

NRMRL, OAQPS, OTAQ, and NCER. We acknowledge

helpful contributions from Tad Kleindienst, Chris Geron,

Kristen Foley, Heather Simon, Nancy Hwang, Charles

Chang, Lucille Bender, and Ruen Tang. We acknowledge

Rob Griffin for verifying data and methods used in his

1999 JGR article and Rohit Mathur and Bill Benjey for

administrative support. Bonyoung Koo, Greg Yarwood,

Ralph Morris provided helpful discussions during the

development of the new SOA module. Disclaimer: The

United States Environmental Protection Agency through

its Office of Research and Development funded and

managed the research described here. It has been subjected

to Agency’s administrative review and approved for

publication.

Supporting Information Available

Additional details, references, tables, and figures. This

material is available free of charge via the Internet at http://

pubs.acs.org.

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