# 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 TEPA 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 in ternal 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 NO X ; (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 SOCratios 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 PM 2.5 – 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 Y j SOA – SOA yield for hydrocarbon precursor, j ∆ H j – Enthalpy of vaporization for SOA from precursor, j ∆ [HC] – Reacted hydrocarbon concentration4

∆ [SOA] – Formed SOA concentration 14 C – Radioactive Carbon H + – Hydrogen ion HO 2 – Hydroperoxyl radical NO – Nitric oxide NO 3 – Nitrate radical NO X – Oxides of nitrogen O 3 – Ozone OH – Hydroxyl radical5



### 1. Introduction

Organic aerosol, both directly emitted into the troposphere and formed by SOA processes, contributes significantly to PM 2.5 concentrations. Over the past two decades, results from a variety of studies show increasing PM 2.5 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 PM 2.5 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 PM 2.5 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 PM 2.5 , formed by secondary processes including gas phase oxidations of NO X compounds and SO 2 in the presence of NH 3 (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 (O 3 ), hydroxyl radicals (OH), and nitrate radicals (NO 3 ), forming semivolatile and nonvolatile oxidation products that can be absorbed into the organic fraction of pre-existing PM 2.5 or undergo nucleation reactions forming new particles. These absorbed oxidation products may6

also undergo further reactions within the PM 2.5 , 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 (Y

j SOA ) (Odum et al., 1996 ; Odum et al., 1997). In this new mechanism, Y j SOA 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 O 3 , OH, and NO 3 with the hydrocarbon, and (2) the temperature-dependent gas-aerosol partitioning coefficients of these products (K

jn (T), n =1,2). The equation for Y j SOA for the Odum model is expressed as follows:

Y j SOA = α

j1 K j1 (T)M 0 /(1 + K

j1 (T)M 0 ) + α

j2 K j2 (T)M 0 /(1 + K

j2 (T)M 0 ) (1)

where

K

jn (T) = K

jn (T ref ) T/T ref exp( ∆ H

j /R(1/T-1/T ref

)), (2)

with M 0 the mass concentration of the absorbing organic aerosol, R the universal gas constant,

∆ H j the enthalpy of vaporization, and T ref the reference temperature at which K jn (T ref ) was7

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

have been conducted to measure " jn and K

jn (T) for a range of atmospherically relevant SOA

precursors.

Over the past decade, the Odum model has been used i n 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 PM 2.5 . 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 μ g 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 that8

are likely to serve as SOA compounds that partition between the gas and aerosol phases. Values for their partitioning coefficients K jn (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 PM 2.5 . 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 of9

vaporization ( ) H j ) 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 PM 2.5 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 K jn for ARO1, ARO2, and TRP1 are emissions-weighted values, while ) H j 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 K

jn can be found in Yu et al., 2007, and references cited therein. In AERO4, SOA formation is initiated by reactions of OH, O 3 , and NO 3 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 ) H j which11

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 ) H j for both BCP and isoprene SOA. Also, as discussed in Section 4, the SOA007 mechanis m 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 aroma tic 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 O 2 , 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 ) H j 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 Ng12

and co-workers, the chamber results were consistent with aromatic peroxy radicals, in the absence of NO, reacting with hydroperoxy radicals (HO 2 ) to form nonvolatile organic aromatic peroxides, which were readily absorbed by pre-existing organic aerosol.

To represent the new aromatic peroxide reaction pa thway, 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 HO 2 , 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/NO X /SO 2 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 t ime evolution of the gas phase and aerosol phase concentrations in molecules cm -3 in the box models, are d[C j ]/dt = J

j /H + CP

j - CL j - (DV

j /H + k dil (t))[C

j ], (3) where [C

j ] and J

j are the concentrations and emission fluxes of compound j, respectively. The mixing height of the mixed layer is H and DV j is the dry deposition velocity. The chemical production and loss rates are CP j and CL

j , 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

k dil (t), which is equal to dlnH/dt for dH/dt > 0.0, otherwise, k dil (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, d escribing 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[G jn ]/dt = β

jn Σ i k ijn [OX i ][HC j ] - (DV g

jn /H + k dil (t)) [G

jn ] (4)

and d[A jn ]/dt = - (DV a

jn /H + k dil (t) + k

poly ) [A

jn ] , (5) where [G

jn ] and [A

jn ] are the gas phase and aerosol phase concentrations, respectively, of semivolatile oxidation product n formed by reaction with SOA precursor hydrocarbon HC j . DV g

jn and DV a

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

from HC

j . In these equations, the mass-based coefficients α jnhave been replaced with the molarbased coefficients β jn 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 HC j with reactive oxidant i (OX i ), to form the semivolatile oxidation product n, is k ijn . The first-order rate constant k poly 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 oxidation15

products is determined by solving the following conservation equations (Equation 6) and the gasaerosol partitioning equations (Equation 7) for G jn and A

jn [A jn ] + [G

jn ] = [SVOC

jn ] (6)

and

K

jn {[POA] + [AROOH] + Σ i,m [A im ]} = [A jn ]/[G jn ], (7) where [SVOC

jn ] is the total concentration of semivolatile oxidation product n formed from HC j . 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 [SVOC jn ], which, after conversion to μ g m -3 , is used to solve Equations 6 and 7 for [A jn ] and [G

jn ]. 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 ( μ g 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

T o 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 fluxes17

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

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

1.39 μ g C m -3 (spring), 1.15 μ g C m -3 (summer), and 1.03 μ g 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 fo r 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) μ g C m -3 , with an annual average TSOC concentration of 0.50

μ g 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 ∆ H j used in the mechanisms. The annual average TRP1 SOC concentrations was 0.97 μ g C m -3 with

Y j SOA = 0.28 with ∆ H j = 156 kj mol -1 for the AERO4 mechanism, but only 0.18 μ g C m -3 with

Y j SOA = 0.04, with ∆ H j = 40 kj mol -1 for the SOA007 mechanism. As shown in Equation 2, with increasing T-T ref , K jn (T), the equilibrium constant for partitioning between the gas and aerosol phases, decreases at a rate controlled by ∆ H j . The lower values for K jn (T), which reflect the18

enhanced evaporation of the semivolatile oxidation products from the aerosol, reduce Y j 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 μ g C m -3 , for AERO4 and SOA007, respectively. As discussed previously, the two mechanisms use different values for the Odum partitioning parameters α

jn , K jn (T ref ), T ref and ∆ H

j . For the AERO4 mechanism, ∆ H j = 156 kj mol -1 , while ∆ H j = 18 kj mol -1 and 32 kj mol -1 for ARO1 and ARO2, respectively, for the SOA007 mechanism. The annual average Y j SOA for ARO for the AERO4 mechanism was 0.10. For the SOA007 mechanism, the annual average ARO Y j SOA of 0.09 included, in addition to the contributions from

t he partitioning two-product Odum model, the AROOH contributions. As expected, the 0.05 μ g C m -3 of BCP SOC and 0.20 μ g 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 Y

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

The thermodynamic parameter ∆ H j 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 K jn (T), which

lowered Y

j 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 of19

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 ∆ H j 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 ∆ H j .

6. Organic Tracer Method for Estimating Ambient SO C 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 PM 2.5 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 SOA20

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 hydrocarbo n 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 14 C 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 PM 2.5 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, β

caryophyllene, 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 dominant21

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 the22

mass fractions.

7. SOC Organic Tracer Data Sets

RTP Data

I n the initial evaluation of the SOC organic tracer method for analyzing ambient PM 2.5 samples, a short-term study was conducted in RTP in the summer of 2003 where eight 48 hour

PM 2.5 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 PM 2.5 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

PM 2.5 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 set23

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 provi de 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 μ g 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 R 2 of 0.73, based on the 47 monthly average concentrations, was 1.00, with an intercept

of -0.054 μ g 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 PM 2.5 .

The data from these two studies provide some infor mation 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 μ g C m -3 on average. On the other hand, the RTP bimonthly TSOC concentrations during the summer (3.7 μ g C m -3 ) were higher than the corresponding MWC concentrations that ranged from 1.2 to 2.8 μ g 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 A MD 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

b ox 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 the26

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 concentr ations, neglecting the JulyAugust data, of 0.44 μ g C m -3 for SOA007, 1.07 μ g C m -3 for AERO4, 1.01 μ g C m -3 for the CMAQ model, and 1.04 μ g 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

I SOC 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 val ue for ∆ H j 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 ∆ H j values were based on measurements of atmospherically relevant smog chamber-generated toluene, α

- pinene and m-xylene SOA samples, while the average value of ∆ H j , for five C 14 -C 18 monocarboxylic acids and two C 5 -C 6 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 ∆ H j values currently used in SOA27

chemistry models. It is our opinion that these data support the ∆ H j values used in the SOA007 mechanism over the 156 kj mol -1 employed in the AERO4 mechanism. However, the ∆ H j 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 k dil (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.

W hile 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 in28

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, O 3 , and NO 3 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 pr esence 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

a cidity, 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 compa red 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 μ g C m -3 for the summer SOC concentrations, with isoprene and monoterpenes contributing about equally to30

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

S OA007 TSOC box model concentration prediction of 0.87 μ g C m -3 , that included SOA007

SOC TRP1 and ISOP concentrations of 0.28 and 0.36 μ g 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

A n 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 l ist 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 ∆ H

j 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 ambient31

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

m easurements. 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 T ref

( ◦ K) α

j1 α

j2 K

j1 ( μ g m -3 ) K j2 ( μ g m -3 ) ∆ H j (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, O 3 , NO 3 1.5

(a) α j1 and α

j2 are mass-based coefficients42

Table 2. Odum Parameters for SOA007 Mechanism (a)

SOA Precursor T ref

( ◦ K) α

j1 α

j2 K

j1 ( μ g m -3 ) K j2 ( μ g m -3 ) ∆ H j (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 O 3 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, O 3 , NO 3 1.5

(a) α j1 and α 2j are mass-based coefficients43

T able 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 0.6644

Table 4. MWC Seasonal and Annual SOC Averages in μ g 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 0.1845

Table 5. SOA007 and RTP 2003 Field Study Bimonthly TSOC and ISOC Concentrations in μ g 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 0.6146

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 uncertain47 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 12SOC (ug/m3)



## aro trp148 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 12SOC (ug/m3)



### aro trp1 bcp isop49 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 6SOC (ug/m3)

toluene a-pinene bcp isoprene50 0 1 2 3 4

1 2 3 4 5 6 7 8 9 10 11 12SOC (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 DATA51 0 2 4 6 8 10

1 2 3 4 5 6 7 8 9 10 11 12OC (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 DATA52 (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 DetroitFigure 6. LINEAR REGRESSION ANALYSIS OF AMBIENT AND ORGANIC TRACERBASED OC CONCENTRATIONS53 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)

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