Updated SOA Chemical Mechanism for the 

Community Multi-Scale Air Quality Model 

R E S E A R C H A N D D E V E L O P M E N T

EPA 600/X-07/025 

September 2007 

www.epa.gov

Updated SOA Chemical 

Mechanism for the Community 

Multi-Scale Air Quality Model 

APM 386 

Edward O. Edney 

Tadeusz E. Kleindienst 

Micheal Lewandowski 

John Offenberg 

Human Exposure and Atmospheric Sciences Division 

National Exposure Research Laboratory 

Research Triangle Park, NC 

U.S. Environmental Protection Agency 

Office of Research and Development 

Washington, DC 20460

Notice: This document is intended for internal Agency use only. Mention of trade names or commercial products does 

not constitute endorsement or recommendation for use. 
1

Updated SOA Chemical Mechanism for the Community Multi-Scale Air 

Quality Model 

E.O. Edney, T.E. Kleindienst, M. Lewandowski, and J.H. Offenberg 

Process Modeling Research Branch, Human Exposure and Atmospheric Sciences 

Division, National Exposure Research Laboratory, U.S. Environmental Protection 

Agency, Research Triangle Park, North Carolina 27511 

Abstract 

An integrated laboratory/field study/modeling program was carried out by scientists in 

the Human Exposure and Atmospheric Sciences Division (HEASD) of the National Exposure 

Research Laboratory (NERL) that included updating the AERO4 secondary organic aerosol 

(SOA) chemical mechanism, currently in the Community Multi-Scale Air Quality (CMAQ) 

model, and evaluating the updated mechanism, the SOA007 SOA chemistry mechanism, using a 

box model. The SOA007 mechanism, after integration into the CMAQ model, will undergo a 

more rigorous evaluation by air quality modelers in the Atmospheric Modeling Division(AMD) 

of the NERL by comparing its predictions with ambient organic carbon (OC) and available 

secondary organic carbon (SOC) concentrations. The new features in the Odum model-based 

SOA007 mechanism include (1) an updated parameterization of the aromatic SOA mechanism 

that takes into account the role of NOX; (2) enthalpies of vaporization measured in the laboratory; 

(3) the two additional high volume biogenic SOA precursors, isoprene and β-caryophyllene, the 

latter of which serves as a surrogate hydrocarbon precursor for sesquiterpenes; (4) SOA to SOC 

ratios measured in the laboratory; (5) a preliminary laboratory-based parameterization for acidcatalyzed SOA formation from isoprene; and (6) an elementary treatment of polymerization 

reactions of semivolatile oxidation products using a single first-order reaction rate constant. 

The SOA007 mechanism, embedded in a box model, was first evaluated by comparing 

its SOC predictions for Research Triangle Park, NC with those obtained for a box model with the 

AERO4 mechanism. The SOA007 SOC predictions were consistent with aromatic hydrocarbons, 
2

isoprene, monoterpenes, and sesquiterpenes all contributing to SOA formation with the highly 

seasonal SOC concentrations increasing significantly during the summer. The AERO4 

mechanism, although containing only biogenic and aromatic SOA precursors, produced a larger 

annual average total SOC concentration than that obtained with the SOA007 mechanism, with the 

SOC concentrations tending to decrease during the summer. An analysis of the modeling results 

indicated the unrealistically high enthalpies of vaporization for monoterpene and aromatic 

hydrocarbon SOA in the AERO4 mechanism were largely responsible for these differences. To 

further evaluate the SOA007 mechanism, its box model SOC concentration predictions as well as 

of those of the AERO4 mechanism in the box model and CMAQ model, were compared with 

estimated ambient SOC concentrations obtained in Research Triangle Park using the organic 

tracer-based method developed during the current program. The trend of increasing SOA007 

SOC concentrations during the summer was supported not only by the Research Triangle Park 

measurements, but also by SOC concentrations measured during this program for samples 

collected in Detroit, MI, Northbrook, IL, Bondville, IL, East St. Louis, IL, and Cincinnati, OH. 

Although the annual average total SOC concentration measured in Research Triangle Park was in 

good agreement with those observed in both the AERO4 box and CMAQ model predictions, that 

was probably due again in part to the high AERO4 enthalpies of vaporization . The SOA007 

annual total SOC concentrations in the box model, on the other hand, could account for 42 % of 

the measured value, which itself is subject to considerable uncertainty. However, because of the 

limitations associated with the elementary treatment of transport in box models, there is apt to be 

significant error in the absolute SOC concentration predictions. Thus, while the inclusion of the 

two additional high volume SOA precursors, improved parameterizations of key SOA-forming 

reactions, and the ability to predict correctly the critical seasonal dependence of SOC 

concentrations, all favor the SOA007 mechanism over the AERO4 mechanism, a final assessment 

of the updated mechanism awaits its evaluation in the CMAQ model that includes a rigorous 

treatment of transport processes. 
3

List of Acronyms 

AERO4 – Current aerosol module in CMAQ 

ALKA1 – Alkanes, Class 1 

ALKA2 – Alkanes, Class 2 

ALKA3 – Alkanes, Class 3 

ALKA4 – Alkanes, Class 4 

ALKA5 – Alkanes, Class 5 

AMD – Atmospheric Modeling Division 

ARO – Aromatic hydrocarbons 

ARO1 – AROmatic hydrocarbons, Class 1 

ARO2 – AROmatic hydrocarbons, Class 2 

AROOH – Aromatic organic peroxide 

ARO2_R – Aromatic organic peroxy radical 

BCP – β-Caryophyllene 

CMAQ – Community Multiscale Air Quality 

CMB – Chemical Mass Balance 

EC – Elemental carbon 

EPA – Environmental Protection Agency 

GEOS – Goddard Earth Observing System 

GC/MS – Gas Chromatograph/Mass Spectroscopy 

HEASD – Human Exposure and Atmospheric Sciences Division 

ISOC – Individual SOC (precursor contribution) 

ISOP – Isoprene 

ISORROPIA – Equilibrium model for multiphase multi-component inorganic aerosols 

LADCO – Lake Michigan Air Directors Consortium 

MM5 – Fifth generation NCAR/Penn State Mesoscale Model 

MWC – Midwestern Cities 

NERL – National Exposure Research Laboratory 

OA – Organic aerosol 

OC – Organic carbon 

OLE1 – OLEfinic hydrocarbons, Class 1 

OLE2 – OLEfinic hydrocarbons, Class 2 

POA – Primary Organic Aerosol 

POC – Primary Organic Carbon 

PM2.5 – Particulate Matter having a diameter less than 2.5 micrometers 

RH – Relative humidity 

RTP – Research Triangle Park 

SAPRC99 – Chemistry mechanism developed at UC-Riverside and released in 1999 

SOA – Secondary Organic Aerosol 

SOA007 – Updated SOA chemical mechanism for CMAQ 

SOC – Secondary Organic Carbon 

SVOC – SemiVolatile Organic Compound 

TPOC – Total Primary Organic Carbon 

TRP1 – Terpenes, Class 1 

TSOC – Total Secondary Organic Carbon 

UW-M – University of Wisconsin, Madison 

UNIFAC – UNIversal quasichemical Functional group Activity Coefficients 

Yj

SOA – SOA yield for hydrocarbon precursor, j 

∆Hj – Enthalpy of vaporization for SOA from precursor, j 

∆[HC] – Reacted hydrocarbon concentration 
4

∆[SOA] – Formed SOA concentration 

14C – Radioactive Carbon 

H

+

 – Hydrogen ion 

HO2 – Hydroperoxyl radical 

NO – Nitric oxide 

NO3 – Nitrate radical 

NOX – Oxides of nitrogen 

O3 – Ozone 

OH – Hydroxyl radical 
5

1. Introduction 

Organic aerosol, both directly emitted into the troposphere and formed by SOA 

processes, contributes significantly to PM2.5 concentrations. Over the past two decades, results 

from a variety of studies show increasing PM2.5 levels lead to higher mortality and morbidity rates 

(Schwartz et al., 1996), decreasing visibility (Sisler and Malm, 1994), and may affect climate 

change (Charlson et al., 1992). Based on these findings, the United States Environmental 

Protection Agency has promulgated National Ambient Air Quality Standards for PM2.5 as a 

means of reducing adverse health and ecological impacts. Air quality models, containing PM 

chemical mechanisms, play an important role in developing cost-effective control strategies for 

reducing ambient PM2.5 levels. 

Efforts to develop chemical mechanisms for predicting the inorganic and organic 

concentrations and compositions of ambient particulate matter have been ongoing for the past 25 

years. Based on the results of research conducted in the 1980s and early 1990s, scientists at the 

University of Miami and at Caltech developed and have refined over the years an advanced 

efficient thermodynamic-based chemical mechanism for predicting inorganic concentrations and 

compositions of PM2.5, formed by secondary processes including gas phase oxidations of NOX

compounds and SO2 in the presence of NH3 (Nenes et al., 1998). However, progress in building 

mechanisms for predicting SOA concentrations and compositions has been considerably slower 

due to the complexity of SOA-forming chemical mechanisms and the lack of instrumentation for 

analyzing highly polar oxygenated SOA compounds. Secondary organic aerosol forms in the 

troposphere when volatile and semivolatile anthropogenic and biogenic hydrocarbons react with 

ozone (O3), hydroxyl radicals (OH), and nitrate radicals (NO3), forming semivolatile and 

nonvolatile oxidation products that can be absorbed into the organic fraction of pre-existing PM2.5

or undergo nucleation reactions forming new particles. These absorbed oxidation products may 
6

also undergo further reactions within the PM2.5, including oligomerization reactions that lead to 

formation of highly complex, multi-functional, polar, difficult to measure organic compounds. 

Although Went (1960) first raised the possibility of organic aerosol forming in the 

troposphere from secondary chemical processes, it was not until the late 1980s when the first 

laboratory experiments in the U.S. were conducted to measure the contribution of a hydrocarbon 

to SOA formation (Grosjean and Seinfeld, 1989). In a series of experiments, the hydrocarbon of 

interest was irradiated in a smog chamber in the presence of NO, with ∆[SOA] and )[HC] 

measured at the end point of each experiment. The SOA yield was defined as ∆[SOA]/)[HC]. 

Based on these measurements, it was concluded that significant SOA is expected to form from 

photooxidation of a number of olefins, alkanes, aromatic hydrocarbons, and monoterpenes. In the 

mid 1990s, however, based on an expanded set of smog chamber data that showed SOA yields 

were functions of the mass concentration of the absorbing organic aerosol and the temperature, 

Odum and co-workers constructed, within the framework of a recent absorption model developed 

by Pankow (1994), an empirical two-product reversible partitioning mechanism for the SOA 

yield (Yj

SOA ) (Odum et al., 1996 ; Odum et al., 1997). In this new mechanism, YjSOA for 

hydrocarbon precursor j depends on (1) the stoichiometric mass-based yields of two hypothetical 

semivolatile oxidation products ("jn, n = 1,2), formed from reactions of O3, OH, and NO3 with the 

hydrocarbon, and (2) the temperature-dependent gas-aerosol partitioning coefficients of these 

products (Kjn(T), n =1,2). The equation for Yj

SOA for the Odum model is expressed as follows: 

Yj

SOA

 = αj1Kj1(T)M0/(1 + Kj1(T)M0) + αj2Kj2(T)M0/(1 + Kj2(T)M0) (1) 

where 

Kjn(T) = Kjn(Tref) T/Tref exp(∆Hj/R(1/T-1/Tref)), (2) 

with M0 the mass concentration of the absorbing organic aerosol, R the universal gas constant, 

∆Hj the enthalpy of vaporization, and Tref the reference temperature at which Kjn(Tref) was 
7

measured (Odum et al., 1996; Sheehan and Bowman, 2001). Since 1995, additional experiments 

have been conducted to measure "jn and Kjn(T) for a range of atmospherically relevant SOA 

precursors. 

Over the past decade, the Odum model has been used in air quality modeling studies of 

SOA formation from aromatic hydrocarbons and monoterpenes (Pun et al., 2003 and 

references cited therein). To date, however, the modeled individual SOA contributions for each 

of the two SOA sources have not been compared with ambient data, largely because of the lack of 

analytical instrumentation for detecting and determining the concentrations of highly polar 

oxygenated SOA compounds in ambient PM2.5. In spite of these limitations, the modeling studies 

allow at least a partial assessment of the predictive capabilities of SOA air quality models 

containing the Odum model by comparing the sum of the modeled primary organic aerosol 

(POA) and SOA concentrations, expressed in μg C m-3, with the measured OC concentrations. 

Based on a number of such modeling studies, it appears biogenic hydrocarbons, in particular 

monoterpenes, contribute significantly to SOA formation, especially in the Southeastern US 

during the summer (Pun et al., 2003). These modeling studies, however, show, in general, only 

modest SOA contributions from photooxidation of aromatic compounds. In summary, the 

consensus in the atmospheric community appears to be that current air quality models, based on a 

Odum model containing only contributions of monoterpenes and aromatic hydrocarbons, 

underestimate SOA contributions in the US. 

To address some of the shortcomings of the empirical Odum model, a number of research 

groups have taken the approach of building SOA chemical mechanisms starting with more 

explicit gas phase chemical mechanisms such as the Master Chemical Mechanism 

(http://mcm.leeds.ac.uk/MCM/project.htt) or the Caltech Atmospheric Chemistry Model (Pun et 

al., 2002). Such exercises require selecting, among the oxidation products, those compounds that 
8

are likely to serve as SOA compounds that partition between the gas and aerosol phases. Values 

for their partitioning coefficients Kjn (T) are estimated using the structure/activity relationshipbased UNIFAC model (Fredenslund et al., 1977), although there are difficulties associated with 

employing such methods to calculate thermodynamic properties of highly polar oxygenated SOA 

compounds. These more explicit mechanisms, however, do provide far more chemical detail than 

that available in the empirical two-product Odum model, although many of proposed reaction 

pathways have yet to be confirmed by laboratory studies. Another shortcoming of the explicit 

mechanism is the extended execution times required for the SOA calculations, which exceed that 

of the Odum model. However, even taking into account the current limitations associated with 

the explicit mechanisms and the fact that SOA formation parameterization in the Odum model is 

apparently more closely tied to experimental data, there is little doubt explicit mechanisms, with 

additional laboratory investigations and more efficient numerical algorithms, will replace the 

Odum model currently in regulatory models. Nonetheless, it appears likely that, in the short term, 

the Odum or a similar model will play a significant role in regulatory assessments of the 

contributions of SOA to the organic fraction of PM2.5. If this is the case, it is important the Odum 

model includes the major SOA contributors and adequately takes into account the temperature 

dependence of the partitioning processes. 

2. Objectives and Approach 

To provide air quality modelers in the NERL AMD the information needed to update the 

AERO4 mechanism, the current mechanism in the CMAQ model, scientists in the NERL 

HEASD have been carrying out an integrated laboratory/field study/modeling SOA chemistry 

research program. The laboratory study has focused on conducting smog chamber experiments to 

identify major SOA hydrocarbon precursors along with their organic tracer compounds. 

Additional laboratory experiments were carried out to (1) measure the effective enthalpy of 
9

vaporization ()Hj) of toluene, m-xylene, and "-pinene SOA; (2) assess the impact of aerosol 

acidity on SOA formation; (3) investigate SOA formation from photooxidation of 

atmospherically relevant hydrocarbon mixtures; and (4) measure the SOA to SOC ratios needed 

to convert SOA concentrations into OC concentrations. Field studies were also conducted where 

ambient PM2.5 samples were collected and analyzed for SOA organic tracer compounds, data 

which were employed to estimate the contributions of anthropogenic and biogenic SOA 

precursors to ambient SOA concentrations. Data from these laboratory and field studies, along 

with updated Odum model parameters from the peer-reviewed scientific literature, were used to 

develop the SOA007 chemical mechanism that could be easily incorporated into CMAQ by AMD 

modelers, without significantly increasing execution times. 

 In this report, the AERO4 mechanism is described and changes implemented in the 

SOA007 mechanism are presented. The impact of the mechanistic changes was evaluated by 

inserting the AERO4 and SOA007 mechanisms in box models and comparing their predictions of 

SOA concentrations in Research Triangle Park (RTP), North Carolina during 2003. The 

SOA007 mechanism model predictions, that were limited by the elementary treatment of 

atmospheric transport within the box model, were then compared with ambient total and 

individual SOA concentrations measured in RTP. The ambient data were also compared with the 

AERO4 box model results and with recent predictions by AMD modelers using the current 

CMAQ model with the AERO4 mechanism. Once the SOA007 mechanism has been 

incorporated into CMAQ, which includes a rigorous treatment of transport processes, a more 

thorough evaluation of the mechanism will be conducted by AMD modelers. While this report 

provides important data supporting replacement of the AERO4 mechanism with the SOA007 

mechanism, a final decision on which SOA mechanism should be used in the CMAQ model 

awaits the AMD assessment. 
10

3. AERO4 and SOA007 SOA Chemical Mechanisms 

 

The AERO4 mechanism, which is described in Yu et al., 2007, is based on the empirical 

Odum two-product SOA model and uses the SAPRC99 gas phase mechanism that includes the 

following lumped and individual hydrocarbons: ARO1, ARO2, TRP1, OLE1, OLE2, ALKA1, 

ALKA2, ALKA3, ALKA4, ALKA5, isoprene (ISOP) and β-caryophyllene (BCP) . Table 1 

shows values for the two-product Odum parameters in AERO4. In AERO4, only ARO1 (MW = 

92 g mol-1) , ARO2 (MW = 106 g mol-1), and TRP1 (MW = 136 g mol-1) contribute to SOA 

formation. The lumped monoterpene hydrocarbon is TRP1, while ARO1 and ARO2, the lumped 

aromatic hydrocarbons, represent high and low yield aromatic hydrocarbons, respectively. In this 

report, the acronym ARO is employed when referring to both ARO1 and ARO2. The lumped 

alkanes, represented by ALKA1, ALKA2, ALKA3, ALKA4, and ALKA5 are differentiated by 

their reactivity as are the lumped olefins OLE1 and OLE2, while all sesquiterpenes are 

represented by BCP (MW = 204 g mol-1). The two-product AERO4 Odum parameters "jn and Kjn

for ARO1, ARO2, and TRP1 are emissions-weighted values, while )Hj

 for each of the ARO1, 

ARO2 and TRP1 semivolatile oxidation products is equal to 156 kj mol-1. Specific information 

on individual aromatic hydrocarbons and monoterpenes, included in the emissions in AERO4, 

and how they were used to calculate "jn and Kjn can be found in Yu et al., 2007, and references 

cited therein. In AERO4, SOA formation is initiated by reactions of OH, O3, and NO3 reactions 

with TRP1 and OH reactions with ARO1 and ARO2. 

In SOA007, two more hydrocarbons have been added to the list of SOA sources, ISOP 

(MW = 68 g mol-1) and BCP (MW = 204 g mol-1), bringing to five the number of SOA sources in 

the new mechanism. The updated Odum parameters for SOA007 are presented in Table 2. The 

two-product Odum parameters for TRP1 are the same as those in AERO4, except for )Hj which 
11

has been replaced by 40 kj mol-1, the value recently measured in our laboratory for "-pinene SOA 

(Offenberg et al., 2006). For BCP, a one-product Odum model with the parameters 

recommended by Griffin et al. (1999), has been employed to describe SOA formation from 

ozonolysis reactions. The two-product Odum parameters for ISOP recommended by Henze and 

Seinfeld (2006) are based on the SOA yield measurements of Kroll et al. (2005; 2006). 

According to Henze and Seinfeld (2006), OH reactions with isoprene are responsible for isoprene 

SOA. In the absence of experimental data, 40 kj mol-1 was assumed for )Hj

 for both BCP and 

isoprene SOA. Also, as discussed in Section 4, the SOA007 mechanism has been expanded to 

include elementary treatments of the impacts of polymerization reactions and acid-catalyzed 

reactions on SOA formation. 

Updated values for the Odum parameters for the aromatic lumped hydrocarbons ARO1 

and ARO2 are predicated on the results of some very recent smog chamber experiments 

conducted by Ng et al., 2007. In a series of photooxidation experiments, Ng and co-workers 

showed that toluene and m-xylene, surrogate hydrocarbons for ARO1 and ARO2 in SOA007, 

respectively, produced SOA in both the absence and the presence of NO. In their mechanism, in 

the presence of NO, aromatic peroxy radicals, formed from reactions of OH with the aromatic 

hydrocarbon in the presence of O2, react with NO to form semivolatile SOA compounds, whose 

reversible partitioning between the gas and aerosol phases is described by the two-product Odum 

model. The updated values for the ARO1 and ARO2 Odum parameters for this pathway are 

displayed in Table 2. The new values of 18 kj mol-1 for ARO1 and 32 kj mol-1 for ARO2 for )Hj

are based on the recent measurements of these parameters in our laboratory (Offenberg et al., 

2006). In this same study, a second pathway for aromatic SOA formation was identified. In a 

series of smog chamber experiments, toluene and m-xylene were irradiated individually in the 

presence of hydrogen peroxide, that readily photolyzes forming OH radicals. According to Ng 
12

and co-workers, the chamber results were consistent with aromatic peroxy radicals, in the absence 

of NO, reacting with hydroperoxy radicals (HO2) to form nonvolatile organic aromatic peroxides, 

which were readily absorbed by pre-existing organic aerosol. 

 To represent the new aromatic peroxide reaction pathway, SAPRC99 was modified 

because, in its present form, only the total organic peroxide concentration is calculated. To 

address this issue, (1) the aromatic contribution to the RO2_R radical, that serves as a radical 

operator, was removed and treated separately as ARO2_R and (2) the formation of the 

nonvolatile aromatic organic peroxide AROOH was added. The gas phase production and loss 

terms assumed for the two compounds are 

ARO2_R 

PROD = 0.765 rk(202) [OH] [ARO1] + 0.804 rk(203) [OH] [ARO2] 

LOSS = rk(51) [ARO2_R] [NO] + rk(52) [ARO2_R] [HO2] 

and 

AROOH 

PROD = rk(52) [ARO2_R] [HO2] 

LOSS = 0.0. 

In this simplified mechanism, ARO2_R reacts only with NO and HO2, with the latter reaction 

forming the nonvolatile SOA compound AROOH (MW = 200 g mol-1), that immediately is 

irreversibly absorbed into the organic fraction of pre-existing aerosol. The rate constant for each 

of the reactions involving ARO2_R was assumed to be equal to that of the corresponding RO2_R 

reactions. 

 
13

4. AERO4 and SOA007 Box Model Descriptions 

In the elementary SOA007 and AERO4 one-box models, the concentrations of gas phase 

and aerosol phase compounds, within the mixed layer, are determined by solving a set of coupled 

differential-algebraic equations. The box model concentrations in molecules cm-3 are calculated 

by solving the differential equations based on the SAPRC99 HC/NOX/SO2 mechanism for 

chemical production and reactions of gas and aerosol phase compounds, with added terms for 

taking into account their ground-based emissions, within the cell, and losses by dry deposition, as 

well as by dilution caused by mixing with clean air during the daily increases in the height of the 

mixed layer. In the model, the differential equations describing the kinetic processes are coupled 

to algebraic equations for calculating gas and aerosol phase concentrations of the Odum 

semivolatile oxidation products that partition reversibly between the two phases. The 

differential-algebraic equations are solved using LSODE, a Fortran code for solving systems of 

nonlinear differential equations and HYBRD, a Fortran code for solving systems of algebraic 

equations. Finally, the ISORROPIA model is employed to determine the inorganic 

concentrations and compositions of the aerosol (Nenes et al., 1998). 

The generic differential equations, describing the time evolution of the gas phase and 

aerosol phase concentrations in molecules cm-3 in the box models, are 

d[Cj]/dt = Jj

/H + CPj - CLj - (DVj /H + kdil(t))[Cj], (3) 

where [Cj ] and Jj are the concentrations and emission fluxes of compound j, respectively. The 

mixing height of the mixed layer is H and DVj is the dry deposition velocity. The chemical 

production and loss rates are CPj

 and CLj , respectively. The effective time-dependent, first-order 

rate constant, that takes into account the dilution from mixing of the gas phase and aerosol 

compounds with clean air during the day when the mixing height is increasing, is expressed as 

kdil(t), which is equal to dlnH/dt for dH/dt > 0.0, otherwise, kdil(t) = 0.0. In this box model, 
14

insufficient information is available for including entrainment of compounds aloft. Furthermore, 

with typical eight to ten hour time constants for the above mentioned dilution process, wet 

deposition, with a time constant ranging from three to six days (Chung and Seinfeld, 2002; Hoyle 

et al., 2007) would not have been very significant in these box model calculations and therefore 

was not included. The combined effect of the SOA007 mechanism and wet deposition awaits 

integration of the mechanism into the CMAQ model, which already contains a detailed treatment 

of wet deposition. 

The explicit forms for the differential equations, describing the time evolution of the gas 

phase and aerosol phase concentrations for the Odum semivolatile oxidation products formed 

from photooxidation of SOA precursor hydrocarbons, are 

d[Gjn]/dt = βjn Σi kijn[OXi][HCj] - (DVg

jn /H + kdil(t)) [Gjn] (4) 

and 

d[Ajn]/dt = - (DVa

jn /H + kdil(t) + kpoly) [Ajn] , (5) 

where [Gjn] and [Ajn] are the gas phase and aerosol phase concentrations, respectively, of 

semivolatile oxidation product n formed by reaction with SOA precursor hydrocarbon HCj. DVg

jn

and DVa

jn are the respective gas phase and aerosol phase dry deposition velocities of product n 

from HCj

. In these equations, the mass-based coefficients αjn have been replaced with the molarbased coefficients βjn calculated using molecular weights of 150, 150, 200, 150, 250 

g mol-1, for the semivolatile oxidation products of ARO1, ARO1, TRP1, ISOP, and BCP, 

respectively. The rate constant for the reaction of HCj with reactive oxidant i (OXi), to form the 

semivolatile oxidation product n, is kijn. The first-order rate constant kpoly takes into account 

polymerization of semivolatile oxidation products in the aerosol phase forming nonvolatile 

compounds. In the SOA007 mechanism, the concentrations of these nonvolatile compounds are 

added to the POA concentrations. The impact of partitioning on the semivolatile oxidation 
15

products is determined by solving the following conservation equations (Equation 6) and the gasaerosol partitioning equations (Equation 7) for Gjn and Ajn 

[Ajn] + [Gjn] = [SVOCjn] (6) 

and 

Kjn{[POA] + [AROOH] + Σi,m[Aim]} = [Ajn]/[Gjn], (7) 

where [SVOCjn] is the total concentration of semivolatile oxidation product n formed from HCj. 

Equation 7 is an explicit form of the general gas-aerosol partitioning equation K[OA] = [A]/[G], 

where [OA] is the total absorbing organic aerosol concentration, which is the sum of the POA 

concentration and the aerosol concentrations of the partitioning semivolatile products. In the 

SOA007 mechanism, the AROOH contribution is also included in the OA concentration. 

To solve the coupled set of equations self-consistently, the following algorithm is 

employed. First, the set of differential equations is solved for gas phase and aerosol phase 

concentrations in molecules cm-3 over a one hour time period. For each semivolatile oxidation 

product n and SOA precursor j, the gas phase and aerosol phase concentrations at the end of the 

hour are added together giving [SVOCjn], which, after conversion to μg m-3, is used to solve 

Equations 6 and 7 for [Ajn] and [Gjn]. At this time, the inorganic aerosol concentrations are also 

updated using the ISORROPIA model. The individual SOA concentrations are multiplied by their 

SOC to SOA ratios to convert SOA concentrations into SOC concentrations (μg C m-3), which 

can be compared with ambient OC concentrations. The impact of acid-catalyzed reactions can be 

taken into account in SOA007, albeit in a highly approximate way, by using the following 

laboratory-derived empirical relationship, reported by Surratt et al.,2007, 

[SOC]isop = [SOC]isop

0 (1 + 0.00361 [H+

]) (8) 

where [SOC]isop

0

 and [SOC]isop are the ISOP SOC concentrations in the absence and presence of 

[H+

], the air concentration of the aerosol hydrogen ion concentration in nmol m-3, respectively. 
16

Finally, in the calculation, the mass-based SOC concentrations are converted back to molecules 

cm-3 and the process is repeated for the next time step. 

5. AERO4 and SOA007 Box Model Simulations 

To compare the performances of the AERO4 and SOA007 mechanisms in box models, 

calculations were carried out to predict individual SOC (ISOC) and total SOC (TSOC) 

concentrations in RTP during 2003. Hourly temperatures, relative humidities, and mixing heights 

were obtained from the MM5 mesoscale model. All of the emissions data were based on the 

2001 Clean Air Interstate Rule inventory except for isoprene, monoterpene, and sesquiterpene 

emissions that were calculated using algorithms for a 36-km square grid cell containing RTP 

provided by Christopher Geron (2007). The gas phase and aerosol phase deposition velocities 

were determined using algorithms in Version 4.5 of the CMAQ model. Monthly average values 

for the meteorological, deposition, and emissions data were calculated as follows. For each 

month of the year, monthly averages for each hour of the day were calculated for temperature, 

relative humidity, and the height of the mixed layer as well as for dry deposition velocities and 

emission rates for each compound. Seven day AERO4 and SOA007 box model calculations were 

then carried out for each of the 12 months in 2003 using the monthly averaged hourly values. 

Model output parameters for the two mechanisms, including the emission rates and ISOC and 

TSOC concentrations, were averaged over the final 24 hours of the simulation and used to 

compare the performances of the two SOA mechanisms. The model simulations did not include 

contributions from acid-catalyzed and polymerization reactions. 

The monthly average ARO1, ARO2, BCP, ISOP, and TRP1 emission fluxes for RTP 

during 2003 are presented in Table 3. While the anthropogenic ARO emissions were relatively 

constant during the year, the temperature-dependent biogenic BCP, ISOP, and TRP1 fluxes 
17

increased significantly during the summer months, tending to dominate aromatic sources. For 

example, the ISOP flux of 15.0 g m-2 yr-1 was about 15 times the emission rate of anthropogenic 

aromatic hydrocarbons during August. 

 The AERO4 monthly average ISOC and TSOC concentrations, which contain 

contributions only from ARO and TRP1, are shown in Figure 1. The annual average AERO4 

TSOC concentration was 1.06 μg C m-3, with seasonal concentrations of 0.69 μg C m-3 (winter), 

1.39 μg C m-3 (spring), 1.15 μg C m-3 (summer), and 1.03 μg C m-3 (fall). During the winter 

months, on average, the ARO and TRP1 SOC contributions were 27 % and 73 %, respectively, 

shifting to 3 % ARO and 97 % TRP1 in the summer. 

 The monthly average ISOC and TSOC concentrations for the SOA007 simulations are 

presented in Figure 2. The seasonal TSOC concentrations were 0.22 (winter), 0.50 (spring), 0.87 

(summer), and 0.41 (fall) μg C m-3, with an annual average TSOC concentration of 0.50 

μg C m-3, which was 47 % of the corresponding value for AERO4. In the winter, the TSOC 

composition was 17 % ARO, 43 % TRP1, 7 % BCP, and 33 % ISOP, while that of the summer 

was 12 % ARO, 33 % TRP1, 13 % BCP, and 42 % ISOP. 

The annual average TSOC concentration, predicted by the AERO4 mechanism, exceeded 

that of the SOA007 mechanism, due in part to the different values for ∆Hj used in the 

mechanisms. The annual average TRP1 SOC concentrations was 0.97 μg C m-3 with 

Yj

SOA = 0.28 with ∆Hj

 = 156 kj mol-1 for the AERO4 mechanism, but only 0.18 μg C m-3 with 

Yj

SOA = 0.04, with ∆Hj

 = 40 kj mol-1 for the SOA007 mechanism. As shown in Equation 2, with 

increasing T-Tref, Kjn(T), the equilibrium constant for partitioning between the gas and aerosol 

phases, decreases at a rate controlled by ∆Hj

. The lower values for Kjn(T), which reflect the 
18

enhanced evaporation of the semivolatile oxidation products from the aerosol, reduce Yj

SOA

, 

which leads to the lower SOC concentrations. 

The annual average ARO SOC concentrations, which are equal to the sum of the ARO1 

and ARO2 concentrations, were 0.10 and 0.07 μg C m-3, for AERO4 and SOA007, respectively. 

As discussed previously, the two mechanisms use different values for the Odum partitioning 

parameters αjn, Kjn(Tref), Tref and ∆Hj

. For the AERO4 mechanism, ∆Hj = 156 kj mol-1, while ∆Hj 

= 18 kj mol-1 and 32 kj mol-1 for ARO1 and ARO2, respectively, for the SOA007 mechanism. 

The annual average Yj

SOA for ARO for the AERO4 mechanism was 0.10. For the SOA007 

mechanism, the annual average ARO Yj

SOA of 0.09 included, in addition to the contributions from 

the partitioning two-product Odum model, the AROOH contributions. As expected, the 0.05 μg 

C m-3 of BCP SOC and 0.20 μg C m-3 of ISOP SOC for the two new biogenic sources in the 

SOA007 mechanism were significant contributions to the annual average TSOC concentration, 

largely due to their enhanced hydrocarbon precursor emissions during the summer. The annual 

average Yj

SOA values were 0.03 and 0.21, for ISOP and BCP, respectively. 

The thermodynamic parameter ∆Hj also plays a role in determining the seasonal 

dependence of the SOC concentration formed through gas-aerosol partitioning. For the AERO4 

ARO, whose emission rates during 2003 in Research Triangle Park were essentially constant 

throughout the year, the increasing temperatures during the summer led to lower Kjn(T), which 

lowered Yj

SOA, which, as shown in Figure 1, resulted in substantially reduced SOC 

concentrations. On the other hand for the SOA007 mechanism, even though the SOC 

contribution from the partitioning reaction pathway decreased during the summer, the increasing 

AROOH SOC concentration due to increased photochemical activity led, as displayed in Figure 

2, to an overall increase in the ARO SOC concentration. For the biogenic SOA hydrocarbon 

precursors, reductions in SOA007 SOC concentrations through enhanced evaporation of 
19

semivolatile compounds from the aerosol were more than compensated for by the increases in 

BCP, ISOP, and TRP1 emissions rates, during the summer. However, for the AERO4 

mechanism, the larger ∆Hj for TRP1 led to summertime evaporative losses that were not fully 

compensated for by the increased TRP1 emissions, resulting in some reduction in the TRP1 SOC 

concentration. In general, whether the SOC concentration for a SOA precursor increases during 

the warm weather months depends on whether the increased emissions, and in the case of the 

ARO, also the enhanced AROOH contributions, are sufficient to offset the evaporative losses that 

increase for increasing ∆Hj

. 

6. Organic Tracer Method for Estimating Ambient SOC Concentrations 

 Evaluation of an SOA mechanism requires a data set of ambient SOC measurements for 

each of the major SOA sources. However, our review of the literature revealed that no such data 

existed, largely due to the lack of analytical instrumentation for identifying and determining the 

concentrations of polar SOA compounds in ambient PM2.5 samples. In the absence of such data, 

the HEASD research program was expanded to assess whether a technique, similar to the tracerbased primary organic carbon (POC) Carbon Mass Balance source-receptor method, could be 

used to estimate the contributions of major SOA sources (Kleindienst et al., 2007). The proposed 

method consisted of first conducting laboratory smog chamber experiments where each SOA 

hydrocarbon precursor was irradiated in the presence of NO in one atmosphere of clean air. The 

collected SOA was analyzed for mass by gravimetric methods and OC with a semi-continuous 

OC/EC monitor. Derivative-based gas chromatography/mass spectroscopy (GC/MS) methods 

were used to identify possible tracer compounds and to measure their mass concentrations. A 

SOA mass fraction for the precursor was obtained by dividing the sum of the tracer compound 

concentrations by the SOA mass concentration. The corresponding SOC mass fraction was 

determined by multiplying the SOA mass fraction by the ratio of the SOC to the SOA 
20

concentrations. To date, mass fractions have been measured in the laboratory for toluene, 

isoprene, β-caryophyllene, and α-pinene. 

In determining the SOC contribution for a hydrocarbon precursor to the ambient OC 

concentration by dividing the sum of its organic tracer concentrations by the laboratory-based 

SOC mass fraction, it has been assumed the SOC mass fraction of the ambient sample is equal to 

that measured in the laboratory, even though it is clearly an approximation. In effect, the 

assumption is made that the complex chemical mechanism that determines the SOC mass fraction 

for a given precursor in the troposphere, for a variety of meteorological conditions, that contains a 

wide range of ambient hydrocarbon, oxidant, and aerosol concentrations, can be reduced to a 

single-valued mass fraction measured in the laboratory under a restricted set of conditions. To 

date, this assumption has been evaluated in the laboratory by comparing toluene and α-pinene 

organic tracer-based SOC concentrations, generated in a toluene/α-pinene/NO smog chamber 

irradiation experiment, with concentrations measured using a 14C method (Offenberg et al., 2007). 

The results showed reasonable agreement between the two methods. In a further evaluation, 

whose results are discussed below, the sum of tracer-based POC and SOC concentrations, 

measured in ambient PM2.5 samples collected in five Midwestern cities (MWC) in the US, were 

compared with measured OC concentrations and it was found that most of the OC could be 

accounted for by the POC and SOC sources. 

 

Presently, available SOC mass fractions are limited to those measured for α-pinene, βcaryophyllene, isoprene, and toluene, and do not include mass fractions for other high volume 

monoterpene, sesquiterpene, and aromatic hydrocarbon SOA precursors. Eventually, organic 

tracers and mass fractions will be needed for these other additional hydrocarbons to identify the 

major sources contributing significantly to SOA formation in RTP and other locations throughout 

the US. However, α-pinene is the major monoterpene emitted from loblolly pine, the dominant 
21

pine species in the Southern U.S. (Geron et al., 2000), and is expected to be the major source of 

monoterpene SOA. In addition, because many of the α-pinene tracer compounds have been 

observed in laboratory generated β-pinene and d-limonene SOA (Jaoui et al., 2005), it is also 

likely the "-pinene SOC concentrations already contain some contributions from these other high 

volume monoterpenes. β-Caryophyllene, a hydrocarbon with a large SOA yield (Griffin et al., 

1999), is the major sesquiterpene emitted by loblolly pine, constituting 66 % of the sesquiterpene 

emissions in RTP (Helmig et al., 2007). In terms of SOA-forming aromatic compounds, mobile 

emissions are the major sources, with toluene accounting for as much as 40 % of the aromatic 

emissions (Harley et al., 1992). Based on a smog chamber study, Kleindienst et al. (2002) 

reported toluene SOA accounted for 50 % of the aromatic SOA from a synthetic gasoline exhaust 

mixture. To assess the impact of other aromatic hydrocarbons, a series of smog chamber 

irradiation experiments are underway in our laboratory to identify organic tracers and calculate 

mass fractions for o-, m-, and p-xylenes, 1,2,4-trimethylbenzene, ethylbenzene and benzene. 

Based on our preliminary analysis, it appears the toluene tracer could also be forming during 

photooxidation of some of the xylenes. If this is the case, then the xylene isomers may be 

included in the toluene SOC concentrations. So while insufficient data are available to estimate 

the SOC concentrations of monoterpene, sesquiterpene and aromatic classes of hydrocarbons in 

RTP, it seems likely the contributions of α-pinene, β-caryophyllene, and toluene themselves may 

account for a significant portion of SOA formed from each hydrocarbon class, respectively. For 

this reason, in these initial applications of the method, we are testing the hypothesis that assumes 

α-pinene, β-caryophyllene, and toluene SOC concentrations, as measured by the present organic 

tracers, represent TRP1, BCP, and ARO SOC concentrations, respectively. Although further 

investigations of the SOC concentrations from other monoterpenes, sesquiterpenes, and aromatic 

hydrocarbons are clearly warranted and currently underway in our laboratory, it is possible these 

additional contributions at this point could be contained in the reported 40 % uncertainty in the 
22

mass fractions. 

7. SOC Organic Tracer Data Sets 

RTP Data 

In the initial evaluation of the SOC organic tracer method for analyzing ambient PM2.5

samples, a short-term study was conducted in RTP in the summer of 2003 where eight 48 hour 

PM2.5 samples were collected and analyzed for organic tracer and OC concentrations. In this 

study, samples were collected only during high pollution days. Derivative-based GC/MS 

analyses of these samples indicated the presence of organic tracers previously identified in the 

laboratory studies. During 2003, a second PM2.5 field study, conducted concurrently at this same 

location by another EPA research group, involved collecting weekend and weekday integrated 

samples that were analyzed for OC concentrations. Twenty-five of the filters from the second 

study were analyzed for organic tracers. The OC and organic tracer concentrations from the two 

field studies were combined into one data set for the entire year of 2003, although the July and 

August samples, which were collected on high pollution days, introduced bias into the data set 

(Kleindienst et al., 2007). A stacked bar chart containing the bimonthly average TSOC and ISOC 

concentrations for the toluene, α-pinene, β-caryophyllene, and isoprene SOA contributions are 

displayed in Figure 3. 

 

 MWC Data 

PM2.5 samples, collected during a Lake Michigan Air Directors Consortium (LADCO)-

sponsored study in five MWC in the US by researchers at the University of Wisconsin-Madison 

(UW-M), were analyzed for the SOA organic tracers at EPA (Lewandowski et al.,2007). 

Samples, collected every sixth day from March 2004 to February 2005 were assembled into 

monthly composites and analyzed at the UW-M for OC and POA organic tracers. This data set 
23

was then used to compare the sum of the TSOC and total primary organic carbon (TPOC) tracerbased concentrations with the measured OC concentrations. Samples were collected in 

Bondville, Illinois, a rural site and at four urban sites: Detroit, Michigan; Cincinnati, Ohio; 

Northbrook, Illinois; and East St. Louis, Illinois. In Figure 4, a stacked bar chart is displayed 

showing the TSOC and ISOC concentrations for the monthly composite samples. The annual and 

seasonal ISOC concentrations are presented in Table 4. Figure 5 shows a stacked bar chart 

containing the monthly TSOC and TPOC concentrations along with the measured OC 

concentrations for each of the five cities. 

The field results from the RTP and MWC studies provide chemical composition data for 

ambient SOA samples that have, until now, not been available. The RTP bimonthly and MWC 

monthly ambient SOC concentration data presented in Figures 3 and 4 show that aromatics, 

sesquiterpenes, monoterpenes, and isoprene all contributed to SOA formation with highly 

seasonal biogenic SOC contributions. The comparison between the sum of the TPOC and TSOC 

concentrations with the measured OC concentration for the MWC study indicate the two 

variables were reasonably well correlated, with the differences in the concentrations falling 

within 1 μg C m-3 for 51 of the 60 composite samples. Most of the discrepancies were found in 

the East St. Louis results, where the predicted OC concentration commonly exceeded the 

measured value. What is particularly striking about the St Louis data is the high levels of isoprene 

SOC observed, which are likely due to large isoprene emissions from a high altitude hardwood 

forest, which has been referred to as an isoprene volcano, located in the Southern Ozark 

Mountains in Southern Missouri and Northern Arkansas (Wiedinmyer et al., 2005). Although the 

mass balance for the OC is poor in St. Louis, it is encouraging to have observed the expected high 

levels of isoprene SOA. 
24

These data were further investigated by a linear regression analysis of the tracer-based 

and measured OC concentrations, presented in Figure 6, that included data for Bondville, 

Northbrook, Cincinnati, and Detroit. The data from East St. Louis were omitted due to the 

frequent overestimation of the measured OC described above. The slope of the regression line 

with an R2

 of 0.73, based on the 47 monthly average concentrations, was 1.00, with an intercept 

of -0.054 μg C m-3. Although there are still numerous uncertainties associated with the SOA 

tracer method, the strong correlation between the sum of the TPOC and TSOC concentrations and 

the measured OC concentrations at these four sampling sites suggest the SOA tracer technique 

may be a valuable addition to the more established CMB method for source apportionment of 

ambient PM2.5. 

 The data from these two studies provide some information on the spatial variability and 

seasonal dependence of SOC compositions and concentrations across the eastern US, although it 

is limited by the RTP and MWC samples being collected during different years. The RTP TSOC 

concentrations during the winter months were similar to the MWC concentrations, which were 

about 0.4 μg C m-3 on average. On the other hand, the RTP bimonthly TSOC concentrations 

during the summer (3.7 μg C m-3) were higher than the corresponding MWC concentrations that 

ranged from 1.2 to 2.8 μg C m-3, which is due in part to the sampling on high pollution days 

during the summer in the RTP study. The RTP SOC accounted for 18 % of the measured OC 

during the winter and 69 % in the summer, while, on average, the MWC SOC contributed 17 % 

and 53 % of the measured OC in the winter and summer, respectively. Furthermore, 82 % of the 

annual average SOC concentration for the RTP study was attributed to the biogenic sources, with 

the remaining 18 % due to aromatics. In the MWC study, 13 % to 19 % of the corresponding 

SOC in the five locations was accounted for by aromatics. 
25

8. Comparison of Ambient SOC Concentrations with Model Predictions 

A full evaluation of the SOA007 mechanism will be conducted by AMD modelers once it 

has been integrated into the CMAQ model where the roles of atmospheric transport and wet 

deposition can adequately be addressed. Within the HEASD program, the model evaluation 

studies were limited to comparing SOA007 box model annual and bimonthly average ISOC and 

TSOC concentrations with the 2003 RTP ambient organic tracer-based measurements. Some 

additional information was obtained by comparing the ambient RTP concentrations with AERO4 

box model predictions and with the results of a recent CMAQ model simulation of SOA 

formation in RTP during 2003, although there were some differences between CMAQ model 

biogenic emissions and those used for the SOA007 and AERO4 box model simulations. 

The CMAQ model SOC concentrations were obtained by AMD modelers using Version 

4.5 of the model which contains 14 vertical layers and uses the SAPRC99 gas phase chemistry 

and the AERO4 SOA mechanism (Byun and Schere, 2006). Time-integrated surface level model 

SOC concentrations were matched to the sampling periods in the 36-km horizontal grid cell 

containing the RTP. The meteorological inputs for the simulation were based on MM5 mesoscale 

model results and the emission inputs obtained from the 2001 Clean Air Interstate Rule inventory. 

The SOC model predictions, RTP ambient SOC data, and comparisons between the 

modeling results and the ambient data are summarized in Tables 5 and 6. The bimonthly average 

TSOC and ISOC concentrations for ARO, BCP, ISOP, and TRP1, obtained using the SOA007 

box model and the organic tracer method, are displayed in Table 5. The fractions of the 

bimonthly average organic tracer-based TSOC and ISOC concentrations, accounted for by the 

SOA007 box model predictions, are displayed in Table 6. And finally, the bimonthly average 

TSOC concentrations for the SOA007 and AERO4 box models, the CMAQ model, and the 
26

ambient RTP data are presented in Figure 7, along with their annual average TSOC 

concentrations. Because of the bias in the ambient July SOC and August TSOC concentrations, 

these data have not been included in the annual averages. 

The tabulated data show annual average TSOC concentrations, neglecting the JulyAugust data, of 0.44 μg C m-3 for SOA007, 1.07 μg C m-3 for AERO4, 1.01 μg C m-3 for the 

CMAQ model, and 1.04 μg C m-3 for the RTP organic tracer-based measurements. The annual 

average SOA007 TSOC concentration accounted for 42 % of the ambient concentration, with the 

ISOC predictions representing 40 %, 29 %, 83 %, and 42 % of the organic tracer-based ARO, 

BCP, ISOP, and TRP1 concentrations, respectively. On the other hand, the annual average 

AERO4 box model and CMAQ model TSOC concentrations accounted for 103 % and 97 %, 

respectively, of the ambient measurement. 

Based on the discussions in Section 5, the large value for ∆Hj in the AERO4 mechanism 

was likely responsible for both the AERO4 box model and CMAQ TSOC concentrations 

exceeding the annual average SOA007 TSOC concentration, which led to the better agreement 

with the ambient concentration. This same large value, however, also caused the AERO4 TSOC 

concentrations to decrease somewhat during the summer months. On the other hand, the SOA007 

SOC concentrations increased during this period, trends that were consistent, not only with the 

ambient concentrations observed in RTP, but also with those in Detroit, Cincinnati, Northbrook, 

Bondville and East St. Louis. Furthermore, the SOA007 ∆Hj

 values were based on measurements 

of atmospherically relevant smog chamber-generated toluene, α-pinene and m-xylene SOA 

samples, while the average value of ∆Hj, for five C14-C18 monocarboxylic acids and two C5-C6

dicarboxylic acids, of 156 kj mol-1 was used to represent all SOA compounds in the AERO4 

mechanism (Tao and McMurray, 1989). Finally, a review of the peer-reviewed literature 

indicated the value of 156 kj mol-1 is significantly larger than ∆Hj

 values currently used in SOA 
27

chemistry models. It is our opinion that these data support the ∆Hj values used in the SOA007 

mechanism over the 156 kj mol-1 employed in the AERO4 mechanism. However, the ∆Hj values 

for ISOP and BCP SOA, that have been set equal to that of α-pinene SOA, still need to be 

measured in the laboratory. 

Limitations in the box model calculations prevented a detailed assessment of the 

significance of the SOA007 mechanism that accounted for only 42 % of the annual average 

ambient RTP TSOC concentration. The SOA007 concentrations, which are average values for 

the mixed layer, were compared only with ground level ambient measurements. Furthermore, 

different averaging techniques were used for the model-simulated and ambient concentrations. 

All emissions were limited to sources within the box, with no compounds, including SOA or 

SOA precursors, transported into the box. The compound losses from the box were largely 

controlled by dilution losses described by the first order rate constant kdil(t). Other than dry 

deposition, no other processes that remove compounds from the box have been included. For 

highly reactive hydrocarbons, such as sesquiterpenes, reactions forming SOA will be sufficiently 

fast so they occur within the box. However, for the more slowly reacting SOA precursors, 

especially aromatic hydrocarbons, a significant fraction of the hydrocarbon can be removed from 

the box before forming SOA. All of these largely transport-related issues will be addressed once 

the SOA007 mechanism has been incorporated into the CMAQ model. 

While the introduction of the SOA007 mechanism into the CMAQ model will improve 

the treatment of SOA formation in the air quality model, a number of major uncertainties will 

remain. As in all modeling studies, emissions data play a dominant role in determining model 

performance. According to Warneke et al., 2007, uncertainties in aromatic emissions could be 

greater than a factor of 2.0, while Christopher Geron, an expert in measuring biogenic emissions 

of hydrocarbons contributing to SOA formation, estimates an uncertainty in isoprene emissions in 
28

the United States of +/- 50 %, with even larger uncertainties of +/- 100-150 % for monoterpenes 

and > +/-150 % for sesquiterpenes, data that clearly limit the predictive powers of SOA models 

(Geron, personal communication). In addition, there could be additional major SOA sources that 

need to be included in the model. In particular, there is evidence that long-chained alkanes 

(Robinson et al., 2007) as well as photochemical reactions of gas phase oxidation products 

dissolved in cloudwater (Carlton et al., 2007) could be significant SOA sources as well. Another 

source of model uncertainty is the SOA to SOC ratio required to convert SOA concentrations to 

SOC . While the SOA007 mechanism uses laboratory measurements for these ratios, questions 

remain whether a single value for this ratio is appropriate for all atmospheric conditions. 

Research is also required to determine the individual contributions of OH, O3, and NO3

reactions with isoprene, monoterpenes and sesquiterpenes to SOA formation. Furthermore, 

questions remain over whether the partitioning-based Odum model is an adequate description of 

the complex set of equilibrium processes and irreversible homogeneous and heterogeneous 

reactions that may control SOA formation under ambient conditions. There is little doubt 

improved methods for detecting polar multifunctional SOA compounds, some of which could be 

of high molecular weight, along with some combination of high-level theoretical calculations and 

new laboratory approaches for establishing and parameterizing the reversible and irreversible 

reactions that form SOA compounds, will be required to address this issue. Until such studies are 

carried out, it seems likely SOA yields will remain a source of considerable uncertainty in SOA 

mechanisms. 

 A variety of studies have shown evidence for the presence of acids enhancing SOA 

formation from monoterpenes and especially isoprene (Jang et al., 2003; Limbeck et al., 2003; 

Iinuma et al., 2004; Kleindienst et al., 2006; Surratt et al., 2007). However, in only one published 

study, where isoprene was irradiated in a smog chamber in the presence of NO and acidic aerosol, 
29

has SOA formation been systematically investigated as a function of the hydrogen ion air 

concentration in the aerosol (Surratt et al., 2007). This investigation, nevertheless, can only be 

considered as preliminary as, for instance, the experiments were conducted only at 30 % RH and 

serious questions remain concerning whether the hydrogen ion air or aqueous concentration 

controls the enhancement in the SOC concentration. For this reason, while Equation 8 may 

provide some measure of the impact of aerosol acidity on ISOP SOA formation, the results must 

be viewed as preliminary. Nonetheless, as a first step in obtaining an estimate of the contribution 

of acid-catalyzed ISOP SOA reactions on TSOC concentrations, the RTP SOA007 box model for 

2003 was rerun, with a constant value for [H+

] of 50 nmol m-3. In the presence of this level of 

acidity, the annual average ISOP SOC concentration increased 18 %. 

Another option introduced into the SOA007 mechanism is the polymerization of 

semivolatile oxidation products to form, by a simple first-order process, nonvolatile compounds 

in the aerosol. Using the recommendation of Kalberer et al. (2004), a constant polymerization 

rate of 2.5 % h-1 was employed for all semivolatile oxidation products in the 2003 RTP SOA007 

box model simulation. The addition of the polymerization reactions increased the annual average 

SOA007 TSOC concentration 11 %. 

Finally, the SOA007 box model predictions were compared with results of a recent air 

quality modeling study that employed the Odum model to predict monoterpene and isoprene SOA 

concentrations. Liao et al., 2007, simulated SOA formation in the southeastern US, using the 

global chemical transport model, the Goddard Earth Observing System (GEOS)-Chem model,

with a SOA mechanism with Odum parameters similar to those in the SOA007 mechanism. The 

reported SOA concentrations, converted to SOC concentrations using the SOA to SOC ratios 

employed in the current study, gave TSOC concentrations ranging from 0.3 to 1.0 μg C m-3 for 

the summer SOC concentrations, with isoprene and monoterpenes contributing about equally to 
30

the concentrations. The Liao SOC concentrations are consistent with the average summertime 

SOA007 TSOC box model concentration prediction of 0.87 μg C m-3, that included SOA007 

SOC TRP1 and ISOP concentrations of 0.28 and 0.36 μg C m-3, respectively. However, as has 

been emphasized in this report, a rigorous evaluation of the SOA007 mechanism ultimately 

requires evaluating the mechanism in a full air quality model rather than in a box model. 

9. Conclusions 

An integrated laboratory/field study/modeling program was carried out to develop the 

SOA chemical mechanism SOA007 to replace the AER04 SOA mechanism currently in the 

CMAQ model. The major improvements in the SOA007 mechanism are (1) an updated 

parameterization of the aromatic SOA mechanism; (2) measurements of ∆H using laboratorygenerated SOA samples; (3) addition of sesquiterpenes and isoprene to the list of SOA 

precursors; (4) measurements of SOA to SOC ratios using laboratory-generated SOA samples; 

and (5) acid-catalyzed reactions for ISOP SOC formation. The new mechanism was evaluated in 

a box model by comparing the SOA007 concentration predictions with those obtained using the 

AERO4 mechanism, the mechanism currently in the CMAQ model. The SOA007 annual average 

TSOC concentration accounted for only 47 % of that obtained using the AERO4 mechanism, 

however the larger AERO4 TSOC concentration appeared to be due to the unrealistically high 

values for ∆Hj

 in the current SOA mechanism. 

 To evaluate the model against ambient field data, an organic tracer-based method for 

estimating ambient TSOC and ISOC concentrations of SOA precursors was developed, thus 

providing, for the first time, an opportunity to assess the performance of an SOA chemical 

mechanism by comparing the ISOC and TSOC model predictions with ambient RTP data. A 

comparison of the SOA007 box model ISOC and TSOC concentration predictions with ambient 
31

data, collected in RTP and in five MWC, showed the model results were consistent with the 

observed increasing summertime trends in ambient TSOC and ISOC concentrations. On the other 

hand, the SOA007 box model could account for 42 % of the annual RTP TSOC concentration 

measurements. The significance of the discrepancy, however, was difficult to assess because of 

the limitations of box models. Only after the SOA007 mechanism has been integrated into the 

CMAQ model by AMD modelers will it be possible to fully evaluate the mechanism. 

Finally, based on this study, it was concluded that further research is required to (1) 

identify additional SOA sources; (2) improve the organic tracer method; (3) measure ∆H values 

for isoprene and sesquiterpenes; (4) obtain more accurate emissions data, especially for biogenic 

hydrocarbons; (5) improve the underpinnings of the SOA mechanisms through theoretical and 

experimental studies; and (6) develop new approaches for analyzing polar multifunctional 

compounds so that a larger portion of the SOA compounds can be identified. 
32

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Disclaimer 

This document is intended for internal use only. Mention of trade names or commercial products 

does not constitute endorsement or recommendation for use. 
40

Acknowledgements 

The authors would like to thank Dr. Prakash Bhave of the NERL AMD for the RTP 2003 

emissions and meteorological data used in the box model simulations as well as the RTP 2003 

CMAQ results obtained using the current AERO4 SOA chemical mechanism. 
41

Table 1. Odum Parameters for AERO4 Mechanism (a)

SOA 

Precursor 

Tref 

(

◦K) 

αj1 αj2 Kj1 

(μg m-3) 

Kj2 

(μg m-3) 

∆Hj

(kj mol-1) 

SOA Radicals SOA/SOC 

 

ARO1 298 0.053 0.0019 0.053 0.0019 156 OH 2.0 

ARO2 298 0.042 0.0045 0.042 0.0045 156 OH 2.0 

TRP1 313 0.086 0.386 0.1172 0.0044 156 OH, O3, NO3 1.5 

(a)

 αj1 and αj2 are mass-based coefficients 
42

Table 2. Odum Parameters for SOA007 Mechanism (a)

SOA 

Precursor 

Tref 

(

◦K) 

αj1 αj2 Kj1 

(μg m-3) 

Kj2 

(μg m-3) 

∆Hj

(kj mol-1) 

SOA Radicals SOA/SOC 

 

ARO1 298 0.058 0.113 0.430 0.047 18 OH 2.0 

ARO2 298 0.031 0.090 0.761 0.029 32 OH 2.0 

BCP 313 1.00 0.0459 40 O3 2.6 

ISOP 298 0.232 0.029 0.00862 1.62 40 OH 1.5 

TRP1 313 0.086 0.386 0.1172 0.0044 40 OH, O3, NO3 1.5 

(a)

 αj1 and α2j are mass-based coefficients 
43

Table 3. Average Monthly RTP SOA Precursor Emission Fluxes in g m-2 yr-1 

Month ISOP TRP1 BCP ARO1 ARO2 

January 0.72 0.38 0.01 0.52 0.66 

February 0.89 0.44 0.02 0.52 0.66 

March 2.14 0.75 0.04 0.55 0.55 

April 3.91 1.11 0.07 0.56 0.53 

May 6.84 1.62 0.13 0.51 0.53 

June 11.7 2.35 0.25 0.57 0.50 

July 14.5 2.74 0.32 0.53 0.47 

August 15.0 2.81 0.33 0.54 0.47 

September 9.60 2.04 0.20 0.50 0.50 

October 5.12 1.33 0.10 0.53 0.52 

November 3.46 1.02 0.06 0.56 0.53 

December 1.04 0.48 0.02 0.51 0.66 
44

Table 4. MWC Seasonal and Annual SOC Averages in μg m-3. 

 Mar-May Jun-Aug Sep-Nov Dec-Feb Annual 

Bondville 

Isoprene 0.25 0.89 0.03 0.02 0.32 

β-Caryophyllene 0.09 0.14 0.12 0.13 0.12 

α-Pinene 0.14 0.42 0.12 0.13 0.21 

Toluene 0.12 0.25 0.09 0.12 0.15 

Northbrook 

Isoprene 0.12 0.50 0.24 0.01 0.22 

β-Caryophyllene 0.18 0.14 0.22 0.21 0.18 

α-Pinene 0.16 0.32 0.16 0.05 0.17 

Toluene 0.12 0.21 0.13 0.06 0.13 

Cincinnati 

Isoprene 0.42 1.27 0.56 0.01 0.56 

β-Caryophyllene 0.09 0.15 0.20 0.14 0.14 

α-Pinene 0.16 0.37 0.16 0.05 0.18 

Toluene 0.16 0.29 0.13 0.02 0.15 

Detroit 

Isoprene 0.07 0.69 0.23 0.00 0.25 

β-Caryophyllene 0.26 0.31 0.41 0.22 0.30 

α-Pinene 0.20 0.51 0.27 0.04 0.25 

Toluene 0.19 0.33 0.18 0.07 0.19 

St. Louis 

Isoprene 0.02 1.05 2.18 0.07 0.90 

β-Caryophyllene 0.06 0.08 0.07 0.14 0.09 

α-Pinene 0.04 0.20 0.32 0.13 0.18 

Toluene 0.06 0.22 0.26 0.15 0.18 
45

Table 5. SOA007 and RTP 2003 Field Study Bimonthly TSOC and ISOC Concentrations in μg m-3. 

 

 

 Month ARO BCP ISOP TRP1 Total 

SOA007 

 

 Jan-Feb 0.04 0.01 0.05 0.09 0.19 

 Mar-Apr 0.06 0.04 0.16 0.20 0.45 

 May-Jun 0.09 0.08 0.28 0.25 0.70 

 Jul-Aug 0.10 0.12 0.38 0.28 0.89 

 Sep-Oct 0.07 0.04 0.18 0.13 0.43 

 Nov-Dec 0.04 0.02 0.10 0.10 0.26 

RTP 

 Jan-Feb 0.12 0.11 0.01 0.22 0.46 

 Mar-Apr 0.30 0.33 0.09 0.39 1.18 

 May-Jun 0.17 0.09 0.79 0.52 1.57 

 Jul-Aug 0.59 0.96 1.34 0.84 3.73 

 Sep-Oct 0.18 0.36 0.24 0.64 1.43 

 Nov-Dec 0.08 0.09 0.24 0.21 0.61 
46

Table 6. Ratios of Bimonthly Average TSOC and ISOC Concentration Predictions to Estimates of Ambient 

Concentrations. 

Month ARO BCP ISOP TRP1 Total 

Jan-Feb 0.34 0.09 ** 0.41 0.41 

Mar-Apr 0.20 0.12 1.78 0.51 0.38 

May-Jun 0.52 0.89 0.35 0.48 0.45 

Jul-Aug 0.18 0.13 0.28 0.33 0.24 

Sep-Oct 0.41 0.11 0.75 0.20 0.30 

Nov-Dec 0.51 0.22 0.42 0.48 0.43 

Annual 0.36 0.26 0.72 0.40 0.37 

Annual* 0.40 0.29 0.82 0.42 0.39 

 * Exclude July and August data 

** Concentration were sufficiently low so that the ratio was highly uncertain
47

Figure 1. RTP MONTHLY AERO4 SIMULATION 

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

1.60

1 2 3 4 5 6 7 8 9 10 11 12

SOC (ug/m3)

aro trp1
48

Figure 2. RTP MONTHLY SOA007 SIMULATION 

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1 2 3 4 5 6 7 8 9 10 11 12

SOC (ug/m3)

aro trp1 bcp isop
49

Figure 3. RTP AMBIENT BIMONTHLY DATA

0.00

0.50

1.00

1.50

2.00

2.50

3.00

3.50

4.00

1 2 3 4 5 6

SOC (ug/m3)

toluene a-pinene bcp isoprene
50

0

1

2

3

4

1 2 3 4 5 6 7 8 9 10 11 12

SOC (ugC m-3

)

Toluene a-Pinene B-Caryophyllene Isoprene

0

1

2

3

Bondville

0

1

2

3

Cincinnati

0

1

2

3

Detroit

East St. Louis

0

1

2

3

Northbrook

Figure 4. MWC AMBIENT MONTHLY SOC DATA 

 
51

0

2

4

6

8

10

1 2 3 4 5 6 7 8 9 10 11 12

OC (ugC m-3

)

SOC POC Measured OC

0

1

2

3

4

Bondville

0

1

2

3

4

5

6

Cincinnati

0

1

2

3

4

5

6

Detroit

East St. Louis

0

1

2

3

4

Northbrook

Figure 5. MWC AMBIENT MONTHLY SOC POC AND OC DATA
52

(POC + SOC) = 1.00 * (Measured OC) - 0.05

R

2

 = 0.73

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

5.0

5.5

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5

Measured OC (ugC m-3)

POC + SOC (ugC m-3

)

Bondville Northbrook

Cincinnati Detroit

Figure 6. LINEAR REGRESSION ANALYSIS OF AMBIENT AND ORGANIC TRACERBASED OC CONCENTRATIONS
53

Figure 7. RTP MODEL PREDICTIONS and AMBIENT DATA

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

Jan-Feb Mar-Apr May-Jun Jul-Aug Sep-Oct Nov-Dec

[TSOC] (ug/m3)

SOA007 CMAQ AERO4 BOX RTP DATA

Annual TSOC Concentrations (ug C m-3)

SOA007 0.44 

AERO4 1.07 

CMAQ 1.01 

RTP 1.04 

Jul-Aug data omitted in averages

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