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Atmospheric Environment 95 (2014) 158e164

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Atmospheric Environment

journal homepage: www.elsevier .com/locate/atmosenv

1.5-Dimensional volatility basis set approach for modelingorganic aerosol in CAMx and CMAQ

Bonyoung Koo a, *, Eladio Knipping b, Greg Yarwood a

a ENVIRON International Corporation, 773 San Marin Drive, Suite 2115, Novato, CA 94998, USAb Electric Power Research Institute, 2000 L Street, NW, Suite 805, Washington, DC 20036, USA

h i g h l i g h t s

* Corresponding author.E-mail address: bkoo@environcorp.com (B. Koo).

http://dx.doi.org/10.1016/j.atmosenv.2014.06.0311352-2310/© 2014 Elsevier Ltd. All rights reserved.

g r a p h i c a l a b s t r a c t

� A new hybrid volatility basis set(VBS) approach to modeling atmo-spheric organic aerosol is developed.

� The 1.5-D VBS scheme adjustsoxidation state as well as volatility inresponse to chemical aging.

� The new scheme is implemented inCAMx and CMAQ and evaluatedagainst ambient data.

a r t i c l e i n f o

Article history:Received 17 December 2013Received in revised form19 May 2014Accepted 16 June 2014Available online 17 June 2014

Keywords:Volatility basis setOrganic aerosolCAMxCMAQ

a b s t r a c t

A hybrid volatility basis set (VBS) approach to modeling atmospheric organic aerosol (OA) is developedthat combines the simplicity of the 1-dimensional (1-D) VBS with the ability to describe evolution of OAin the 2-dimensional space of oxidation state and volatility. This 1.5-D scheme uses four basis sets todescribe varying degrees of oxidation in ambient OA: two basis sets for chemically aged oxygenated OA(anthropogenic and biogenic) and two for freshly emitted OA (from anthropogenic sources and biomassburning). Each basis set has five volatility bins including a zero-volatility bin for essentially non-volatilecompounds. The scheme adjusts oxidation state as well as volatility in response to chemical aging bysimplifying the 2-dimensional VBS model. The 1.5-D VBS module is implemented in two widely usedphotochemical grid models (CAMx and CMAQ) and evaluated for summer and winter 2005 episodes overthe eastern U.S. CAMx performs reasonably well in predicting observed organic carbon (OC) concen-trations while CMAQ under-estimates OC, with differences between models being attributed to sciencealgorithms other than the VBS. Oxygenated OA accounts for less than half of the modeled OA mass inwinter but about 80% of total OA in summer due to more rapid chemical aging in summer.

© 2014 Elsevier Ltd. All rights reserved.

1. Introduction

Atmospheric organic aerosol (OA) is highly complex, anddetailed mechanistic descriptions include hundreds or thousandsof compounds and are impractical for use in large-scale

photochemical grid models (PGMs) (Johnson et al., 2006). There-fore, PGMs adopt simplified OAmodules where organic compoundswith similar properties and/or origin are lumped together. Untilrecently, most PGMs employed a multi-product model introducedby Odum et al. (1996) where multiple (typically two) condensableorganic compounds are produced from oxidation of each hydro-carbon precursor and partitioned into a pseudo-ideal solution viaabsorptive partitioning (Pankow, 1994a,b). Although this approachcan fit smog chamber data with few parameters, the estimated

Fig. 1. 2-D VBS space defined by C* and average oxidation state of carbon (OSC); iso-pleths of carbon numbers (in square boxes) are calculated using the group-contribution method developed by Donahue et al. (2011); IVOCs from biomassburning and anthropogenic sources are placed to have 9 and 12 carbons, respectively;chemical aging (indicated by arrows) accounts both for oxygenation (increase inoxidation state) and fragmentation (decrease in carbon number).

Table 2POA chemical aging scheme.

POA basis set Oxidation reactiona

HOA VAP1 þ OH / 0.864 VAP0 þ 0.142 VAS0VAP2 þ OH / 0.877 VAP1 þ 0.129 VAS1VAP3 þ OH / 0.889 VAP2 þ 0.116 VAS2VAP4 þ OH / 0.869 VAP3 þ 0.137 VAS3

BBOA VFP1 þ OH / 0.538 VFP0 þ 0.464 VBS0VFP2 þ OH / 0.689 VFP1 þ 0.313 VBS1VFP3 þ OH / 0.783 VFP2 þ 0.220 VBS2VFP4 þ OH / 0.846 VFP3 þ 0.156 VBS3

a See the footnote of Table 1 for the model species naming convention.

B. Koo et al. / Atmospheric Environment 95 (2014) 158e164 159

volatilities of the surrogate products were often inconsistent withthe volatility range of ambient OA (Donahue et al., 2009). Anotherlimitation of the 2-product model is that it does not treat volatilitychange in response to chemical aging of secondary OA (SOA). Pri-mary OA (POA), although traditionally treated as non-volatile inPGMs, is mostly semi-volatile under ambient conditions (Lipskyand Robinson, 2006; Shrivastava et al., 2006) and the vapor-phase portion can undergo photochemical oxidation (Robinsonet al., 2007).

The volatility basis set (VBS) approach (Donahue et al., 2006;Robinson et al., 2007) provides a unified framework for gas-aerosol partitioning and chemical aging of both POA and SOA. Ituses a set of semi-volatile OA species with volatility equally spacedin a logarithmic scale (the basis set). VBS member species areallowed to react further in the atmosphere (chemical aging) todescribe volatility changes (i.e., shifting between volatility bins).The VBS approach has been widely adopted in the air qualitymodeling community and implemented in many regional- and

Table 1Molecular properties of volatility bins.

Basis set Model speciesnamea

C* (mg m�3) OSC C# O# H# MW OA/OC

SV-OOA PAS0 & PBS0 0b 0.102 7 4.90 9.10 172 2.05PAS1 & PBS1 1 �0.188 7.25 4.38 10.1 167 1.92PAS2 & PBS2 10 �0.463 7.5 3.84 11.2 163 1.81PAS3 & PBS3 100 �0.724 7.75 3.30 12.2 158 1.70PAS4 & PBS4 1000 �0.973 8 2.74 13.3 153 1.59

HOA PAP0 0b �1.52 17 2.69 31.3 278 1.36PAP1 1 �1.65 17.5 2.02 33.0 275 1.31PAP2 10 �1.78 18 1.34 34.7 272 1.26PAP3 100 �1.90 18.5 0.632 36.4 268 1.21PAP4 1000 �2.00 19 0.0 38.0 266 1.17

BBOA PFP0 0b �0.704 10 4.32 15.7 205 1.71PFP1 1 �1.02 11 3.60 18.4 208 1.58PFP2 10 �1.29 12 2.85 21.1 211 1.47PFP3 100 �1.52 13 2.08 23.9 213 1.37PFP4 1000 �1.73 14 1.27 26.7 215 1.28

a Themodel VBS species name consists of 4 characters that indicate the phase (Pe

particle; V e vapor), the source (A e anthropogenic; B e biogenic; F e fire), theformation (P e primary; S e secondary), and the volatility bin number.

b Properties of the lowest volatility bins were estimated assuming C*¼ 0.1 mgm�3

but they actually represent all OA with C* �0.1 mg m�3, and are treated as non-volatile in the model.

global-scale models including PMCAMx (Lane et al., 2008;Shrivastava et al., 2008; Murphy and Pandis, 2009; Tsimpidi et al.,2010; Fountoukis et al., 2011), CHIMERE (Hodzic et al., 2010;Zhang et al., 2013), WRF-CHEM (Shrivastava et al., 2011;Ahmadov et al., 2012), EMEP (Bergstr€om et al., 2012), COSMO-ART (Athanasopoulou et al., 2013), GISS GCM II (Farina et al.,2010; Jathar et al., 2011), and GEOS-CHEM (Jo et al., 2013). How-ever, the first generation VBS models use one-dimensional basissets (1-D VBS) wherein organic compounds are grouped only byvolatility and thus are unable to describe varying degrees ofoxidation observed in atmospheric OA of similar volatility. A twodimensional VBS (2-D VBS) groups organic compounds by oxida-tion state as well as volatility (Donahue et al., 2011, 2012a) and hasbeen used to model chamber experiments (Jimenez et al., 2009;Donahue et al., 2012b; Chacon-Madrid et al., 2012; Chen et al.,2013) and implemented in a Lagrangian trajectory model (Murphyet al., 2011, 2012) but has yet to be implemented in a PGM becausethe computational burden would be high.

Here we develop a new OA modeling approach that is based onthe 1-D VBS framework but accounts for changes in oxidation stateof OA as well as its volatility using multiple reaction trajectoriesdefined in the 2-D VBS space. This 1.5-D VBS scheme is imple-mented in two widely used PGMs: the Comprehensive Air-qualityModel with Extensions (CAMx; ENVIRON, 2011) and CommunityMultiscale Air Quality (CMAQ) modeling system (Byun and Ching,1999). Both models were applied to simulate OA in summer andwinter months over the eastern U.S. and the model performance isdiscussed.

2. Model description

Ambient aerosol mass spectrometer (AMS) measurement datahave identified four characteristic groups of OA based on oxidationstate: Hydrocarbon-like OA (HOA), biomass-burning OA (BBOA),and semi- and low-volatile oxygenated OA (SV-OOA and LV-OOA)(Jimenez et al., 2009). The VBS framework developed here uses

Table 3Product mass yieldsa for oxidation of hydrocarbon precursors and IVOCs.

Precursor High-NOx yields Low-NOx yields

1 10 100 1000 1 10 100 1000

Benzene 0.003 0.165 0.300 0.435 0.075 0.225 0.375 0.525Toluene 0.011 0.257 0.482 0.718 0.011 0.257 0.750 0.468Xylene 0.002 0.195 0.300 0.435 0.075 0.300 0.375 0.525Isoprene 0.000 0.023 0.015 0.000 0.009 0.030 0.015 0.000Monoterpenes 0.012 0.122 0.201 0.507 0.107 0.092 0.359 0.608Sesquiterpenes 0.075 0.150 0.750 0.509 0.075 0.150 0.750 0.509IVOCs 0.030 0.194 0.264 0.376 0.030 0.194 0.264 0.376

a The yields are fitted for four volatility bins with C* ¼ 1, 10, 100, and1000 mg m�3; SOA density is assumed to be 1.5 g cm�3; VOC/NOx >10 ppbC/ppb isassumed a low NOx condition and VOC/NOx <3 ppbC/ppb a high NOx condition.

Fig. 2. The 36- and 12-km modeling grids.

B. Koo et al. / Atmospheric Environment 95 (2014) 158e164160

four basis sets to represent these ambient OA groups: Two foranthropogenic and biogenic SOA or oxygenated POA (OPOA), onefor fresh POA from anthropogenic sources, and one for fresh POAfrom biomass-burning emissions. Each basis set includes 5 vola-tility bins ranging from 10�1 to 103 mg m�3 in saturation concen-tration (C*), which roughly covers the volatility range of semi-volatile organic compounds (SVOCs). Temperature dependence ofC* is described by the ClausiuseClapeyron equation (Sheehan andBowman, 2001). An effective enthalpy of vaporization (DH) valueof 35 kJ mol�1 is used for all SOA species. For POA, DH is estimatedusing the following empirical formulas:

DH ¼ �4 log10�C*298 K

�þ 85 kJ mol�1

(For biomass burning; May et al., 2013c)

DH ¼ �11 log10�C*298 K

�þ 85 kJ mol�1

(For other primary; Ranjan et al., 2012)The molecular structures of OA compounds assigned to these

volatility bins are determined by placing them on the 2-D volatilityspace defined by Donahue et al. (2011, 2012a). Fig. 1 illustrates theplacement of three basis sets for SV-OOA, HOA and BBOA on the 2-DVBS space. For example, the average oxidation state of carbon (OSC)value for SV-OOA ranges from �1 to 0. The upper bound roughlycrosses the carbon-number isopleth of 7 carbons at

Table 4Volatility distribution factors of POA emissions.

Scenario POA species Emission fraction for volatility bin with C* of

0 1 10 100 1000

Base case POA_GV 0.27 0.15 0.26 0.15 0.17POA_DV 0.03 0.25 0.37 0.24 0.11POA_OP 0.09 0.09 0.14 0.18 0.5POA_BB 0.2 0.1 0.1 0.2 0.4

Mono-POA POA_GVPOA_DVPOA_OPPOA_BB

0.09 0.09 0.14 0.18 0.5

High-POA POA_GVPOA_DVPOA_OP

0.4 0.26 0.4 0.51 1.43

POA_BB 0.27 0.27 0.42 0.54 1.5

C* ¼ 10�1 mg m�3 while the lower bound does the isopleth of 8carbons at C* ¼ 103 mg m�3. We place the OOA volatility bins alongthe line that connects these two points, separated by one decade inC*. Carbon numbers for the in-between volatility bins are linearlyinterpolated between 7 and 8, and their OSC values are determinedusing the group-contribution expression developed by Donahueet al. (2011). Oxygen and hydrogen numbers are then calculatedusing the relationship between OSC and atomic molar ratios (Krollet al., 2011) and the van Krevelen relation (Heald et al., 2010):

OSCy2O : C�H : C

O : Cþ H : Cy2

Table 1 lists molecular properties for all the volatility bins asdetermined above. Oxidation (chemical aging) of SOA and OPOA ismodeled by shifting OA mass along the pre-defined pathway of theOOA basis set (shown as arrows between and in Fig. 1). Liketraditional 1-D VBS models, one oxidation step corresponds to afactor of 10 decrease in C*. However, it also increases oxidation stateas this pathway is defined in the 2-D VBS space; hence, we call thisapproach a “1.5-dimensional” VBS model. Reduction in carbonnumber indicates that fragmentation is implicitly accounted for.Chemical aging of POA transfers OA from the HOA (or BBOA) basisset to the OOA basis set. However, a single oxidation step wouldhardly provide enough carbon number reduction required to makesuch transfer. It is likely that the reaction trajectory of POA aging inthe 2-D VBS space initially follows the carbon-number isopleths(oxygenation) but then transitions to fragmentation of morehighly-oxygenated products (Donahue et al., 2012a). In oursimplified framework, we approximate the POA aging process byusing a “partial conversion” to OOA: Oxidation products of POA arerepresented as a mixture of POA and OPOA in the next lowervolatility bins. Assuming one oxygen is added by a single oxidationstep, the average carbon number of this “mixture” can be calculatedusing the group-contribution expression of Donahue et al. (2011).The branching ratios are then determined using carbon and oxygenbalances (Table 2). A rate constant of 4 � 10�11 cm3 molecule�1 s�1

is used for gas-phase reaction of POA with OH radical (Robinsonet al., 2007). The OH reaction rate for anthropogenic OOA aging isassumed to be 2�10�11 cm3molecule�1 s�1, which is twice the ratepreviously assumed for anthropogenic SOA aging (Murphy andPandis, 2009). Note that the OOA basis set in our scheme treatsOPOA as well as SOA while the rate by Murphy and Pandis (2009)was estimated specifically for aromatic VOC oxidation products.Recent estimation of the OH reactivity for the organics in the 2-DVBS space showed that the OH oxidation rate within the volatilityrange of our VBS scheme would be almost always above2 � 10�11 cm3 molecule�1 s�1 (Donahue et al., 2013). Aging ofbiogenic SOA is disabled in our implementation based on previousmodeling studies that found aging biogenic SOA led to a significantover-prediction of OA in rural areas (Lane et al., 2008; Murphy andPandis, 2009). Uptake of OH radicals in the organic particle phasemay further change volatility and oxygen content of OA. However,heterogeneous oxidation is expected to be much slower than thegas-phase oxidation (Jimenez et al., 2009; Donahue et al., 2012b),and thus not included in our model. Table 3 presents NOx-depen-dent product mass yields from oxidation of hydrocarbon precursors(aromatics, isoprene, monoterpenes and sesquiterpenes; C* � 107)and intermediate-volatility organic compounds (IVOCs;104 � C*� 106) that were determined based on smog chamber data(Murphy and Pandis, 2009; Hildebrandt et al., 2009). While fittingchamber data with the VBS parameters is more robust than the 2-product approach because the volatility bins are fixed (Presto andDonahue, 2006), there still exist uncertainties in the fitted

Fig. 3. Fractional biases of OC modeled by (a) CAMx and (b) CMAQ at the IMPROVE and STN monitoring sites in each RPO region for the winter (February) and summer (August)monthly episodes. The mono-POA case assumes a single volatility distribution for all POA emissions, and the high-POA case increases the existing POA emissions by a factor of 3.

B. Koo et al. / Atmospheric Environment 95 (2014) 158e164 161

parameters. Barsanti et al. (2013) evaluated the VBS parameters ofTsimpidi et al. (2010) and discovered that they could not alwaysreproduce published smog chamber data. Another issue related tothe VBS parameters based on chamber data is that the chamberexperiments often allow continued oxidation (aging) of the first-generation products. Therefore, application of these yieldstogether with a separate aging scheme as described abovemay leadto overestimation of SOA production. This is especially true for ar-omatic precursors where later-generation kinetics may be fasterthan that of the precursors. Henry et al. (2012) discussed experi-mental conditions to isolate the first-generation product yieldsfrom a-pinene oxidation. Further investigations should follow toevaluate the impact of the improved parameters on the modelprediction of SOA formation.

3. Model evaluation

The 1.5-D VBS scheme was implemented in CAMx version 5.41and CMAQ version 5.0.1. The two models were then applied to twomonth-long (February and August) episodes based on the EPA'sCross-State Air Pollution Rule (CSAPR) base year (2005) modeling

database (EPA, 2011a) for model evaluation. The modeling domainconsists of a 36-km horizontal grid covering the entire continentalU.S. and a 12-km nested grid over the eastern U.S. (Fig. 2). Meteo-rological conditions and biogenic emissions were based on theWeather Research and Forecasting (WRF) model version 3.4(Dudhia, 2012) and the Model of Emissions of Gases and Aerosolsfrom Nature (MEGAN) version 2.1 (Guenther et al., 2012), respec-tively. CMAQ version 5 requires emissions of explicit major crustalelements (Al, Ca, Fe, Si, and Ti) which the CSAPR emission inventorywas missing. We split the CSAPR's unspeciated other PM2.5 emis-sions into the required elemental species using the average speci-ation profile of Reff et al. (2009). The 2005 version of Carbon Bondchemistry mechanism (CB05; Yarwood et al., 2005) is used tosimulate oxidant chemistry.

Table 4 lists the POA volatility distributions used in this study.We employed source-specific volatility distribution factors for POAemissions from gasoline vehicles (POA_GV), diesel vehicles(POA_DV), and biomass burning (POA_BB), based on recent cham-ber studies (May et al., 2013a,b,c). For other POA emissions(POA_OP), we applied the distribution factors estimated byRobinson et al. (2007). We also modeled two sensitivity scenarios

Fig. 4. Average mass fractions of individual OA species in total OA from the CAMx base case simulation; see the footnote of Table 1 for the OA species naming convention.

B. Koo et al. / Atmospheric Environment 95 (2014) 158e164162

to assess the impact of uncertainties in the volatility distributionand the traditional POA inventory on the model OC prediction. Themono-POA scenario applied the same volatility distribution (ofRobinson et al.) for all POA emission. The POA volatility distributionfactors for the high-POA scenario are based on the ambient mea-surement data in Mexico City and increases the existing POAemissions by a factor of 3 (Tsimpidi et al., 2010; Shrivastava et al.,2011). Emissions of IVOCs make important contributions to OA inthe atmosphere but generally are missing from emission in-ventories because neither VOC nor POA emission factors accountfor IVOCs. If not provided, IVOC emissions are estimated by the VBSmodule by setting them equal to 1.5 � POA emissions by default, assuggested by Robinson et al. (2007). The IVOC emissions are thenimplicitly increased by a factor of 3 in the high-POA scenario.

The model performance was evaluated using organic carbon(OC) measurement data collected at the Interagency Monitoring ofProtected Visual Environments (IMPROVE) and EPA's SpeciationTrends Network (STN) monitoring sites. The STN OC data wasartifact-corrected to make it consistent with the IMPROVE data(Malm et al., 2011). Modeled OA concentrations are converted to OCusing the OA/OC ratio defined for each model OA species (seeTable 1) to be compared with the observed OC data.We focused ourevaluation on the 12-km modeling domain.

4. Results and discussion

Fig. 3 shows the summer and winter OC performance of the 1.5-D VBS scheme implemented in CAMx and CMAQ. The evaluationwas performed for each of the four U.S. Regional Planning Orga-nization (RPO) regions within the 12-km modeling grid: CentralRegional Air Planning Association (CENRAP), Midwest RegionalPlanning Organization (MRPO), Mid-Atlantic/Northeast VisibilityUnion (MANE-VU), and Visibility Improvement State and TribalAssociation of the Southeast (VISTAS). The CAMx base case scenario

showed a reasonable OC performance with fractional biases within±40% in most cases. In the CENRAP states, winter over-estimationand summer under-estimation of OC are more prominent.Applying the POA volatility distribution of Robinson et al. for allPOA emissions somewhat decreased model-predicted OC as thoseof May et al. tend to weight more on the less volatile bins. Themodeled OC is quite sensitive to the change in POA emissions,especially in winter. Increasing POA emissions by a factor of 3significantly raised the modeled OC level resulting in over-estimation biases in all cases. Although the VBS schemes imple-mented in CAMx and CMAQ are essentially identical, the CMAQmodel exhibits quite different OC performance than CAMxwith theCMAQ base case showing significant under-estimation biases inmany cases. We believe that this discrepancy was caused by modeldifferences outside the OA modules because inert species such aselemental carbon showed corresponding performance differencesbetween the two models (not shown). Similar to CAMx, higher POAemissions dramatically increased CMAQ-predicted OC concentra-tions changing the direction of the bias in most cases. Additionalperformance metrics are given in the Supporting Information(Table S1). Table S1 also includes performance of the OA modulescurrently implemented in CAMx and CMAQ. In summer, the currentOA module in CAMx produced more OC than the base case VBS inthe CENRAP and VISTAS regions where biogenic SOA precursors areabundant. During winter, POA becomes more important and thecurrent schemewith nonvolatile POA tends to overestimate OC. TheVBS model allows POA to evaporate and thus shows betterwintertime performance. Differences between the current OAmodule and VBS in CMAQ are much greater than in CAMx becausethe current CMAQ OA module includes aging of nonvolatile POAwhich adds substantial POA mass (by oxygen incorporation) andconsequently increases SOA mass (because absorptive partitioningtheory predicts increasing SOA yield with increasing POAconcentration).

B. Koo et al. / Atmospheric Environment 95 (2014) 158e164 163

The CAMx base case result shows that OOA (PASþ PBS) accountsfor 40e48% of total OAmass inwinter and 75e79% in summer withhigher OOA predicted at the IMPROVE (rural) sites than at the STN(urban) sites (Fig. 4). CMAQ gives similar OA species fractions toCAMx (Fig. S1). The results qualitatively agree with previous esti-mates based on AMS measurement data at various locations in theNorthern Hemisphere (Zhang et al., 2007; Jimenez et al., 2009)while the AMS-based OOA fractions are somewhat higher than themodeled fractions, especially in the rural/remote areas. BBOA maybe partially included in OOA in the AMS factor analysis (Zhang et al.,2007; Hallquist et al., 2009), but that alone cannot explain thediscrepancy between the model and AMS measurement as theBBOA contribution accounts for only minor fraction of total OA inour simulation. Heterogeneous oxidation or biogenic SOA aging,which our VBS model currently lacks, may increase the OOA frac-tion. However, further study is needed to determine relativeimportance of these processes. Other assumptions such as oxida-tion rate constants and aging pathways defined in the 2-D VBSspace can also affect themodeled fraction of OOA. The large fractionof anthropogenic OA in our base case modeling (especially in urbanareas) appears inconsistent with previous field studies that sug-gested dominant contributions frommodern carbon (De Gouw andJimenez, 2009). This is probably because the CSAPR emission in-ventory used in this study did not properly separate emissions frombiomass burning: The CSAPR fire emissions only contained wild-fires and prescribed burns, and other biomass burning sources suchas agricultural burning and residential wood burning were groupedtogether with other anthropogenic emissions (EPA, 2011b).

The VBS approach reflects recent advancement in our under-standing of atmospheric OA. However, the VBS option has beennotably missing in CAMx and CMAQ, the two most widely usedPGMs in policy-related applications in U.S. Making available thisnew 1.5-D VBS scheme in the two models will provide air qualityplanners with an important tool that better describes the atmo-spheric processes of ambient OA.

Acknowledgments

We thank Neil Donahue, Allen Robinson, and Spyros Pandis atCarnegie Mellon University for valuable discussions on the VBSimplementation. This researchwas supported by the Electric PowerResearch Institute (EPRI) EP-P38705/C17224.

Appendix A. Supplementary data

Supplementary data related to this article can be found at http://dx.doi.org/10.1016/j.atmosenv.2014.06.031.

References

Ahmadov, R., McKeen, S.A., Robinson, A.L., Bahreini, R., Middlebrook, A.M., deGouw, J.A., Meagher, J., Hsie, E.-Y., Edgerton, E., Shaw, S., Trainer, M., 2012.A volatility basis set model for summertime secondary organic aerosols overthe eastern United States in 2006. J. Geophys. Res. 117, D06301.

Athanasopoulou, E., Vogel, H., Vogel, B., Tsimpidi, A.P., Pandis, S.N., Knote, C.,Fountoukis, C., 2013. Modeling the meteorological and chemical effects ofsecondary organic aerosols during an EUCAARI campaign. Atmos. Chem. Phys.13, 625e645.

Barsanti, K.C., Carlton, A.G., Chung, S.H., 2013. Analyzing experimental data andmodel parameters: implications for predictions of SOA using chemical transportmodels. Atmos. Chem. Phys. 13, 12073e12088.

Bergstr€om, R., Denier van der Gon, H.A.C., Pr�evot, A.S.H., Yttri, K.E., Simpson, D.,2012. Modelling of organic aerosols over Europe (2002e2007) using a volatilitybasis set (VBS) framework: application of different assumptions regarding theformation of secondary organic aerosol. Atmos. Chem. Phys. 12, 8499e8527.

Byun, D.W., Ching, J.K.S., 1999. Science Algorithms of the EPA Models-3 CommunityMultiscale Air Quality (CMAQ) Modeling System. Atmospheric Modeling Divi-sion, National Exposure Research Laboratory, U.S. Environmental Protection

Agency, Research Triangle Park, NC. http://www.epa.gov/AMD/Research/CMAQ/CMAQdocumentation.html.

Chacon-Madrid, H.J., Murphy, B.N., Pandis, S.N., Donahue, N.M., 2012. Simulations ofsmog-chamber experiments using the two-dimensional volatility basis set:linear oxygenated precursors. Environ. Sci. Technol. 46, 11179e11186.

Chen, S., Brune, W.H., Lambe, A.T., Davidovits, P., Onasch, T.B., 2013. Modelingorganic aerosol from the oxidation of a-pinene in a Potential Aerosol Mass(PAM) chamber. Atmos. Chem. Phys. 13, 5017e5031.

De Gouw, J., Jimenez, J.L., 2009. Organic aerosols in the Earth's atmosphere. Environ.Sci. Technol. 43, 7614e7618.

Donahue, N.M., Robinson, A.L., Stanier, C.O., Pandis, S.N., 2006. Coupled partitioning,dilution, and chemical aging of semivolatile organics. Environ. Sci. Technol. 40,2635e2643.

Donahue, N.M., Robinson, A.L., Pandis, S.N., 2009. Atmospheric organic particulatematter: from smoke to secondary organic aerosol. Atmos. Environ. 43, 97e109.

Donahue, N.M., Epstein, S.A., Pandis, S.N., Robinson, A.L., 2011. A two-dimensionalvolatility basis set: 1. organic-aerosol mixing thermodynamics. Atmos. Chem.Phys. 11, 3303e3318.

Donahue, N.M., Kroll, J.H., Pandis, S.N., Robinson, A.L., 2012a. A two-dimensionalvolatility basis set e part 2: diagnostics of organic-aerosol evolution. Atmos.Chem. Phys. 12, 615e634.

Donahue, N.M., Henry, K.M., Mentel, T.F., Kiendler-Scharr, A., Spindler, C., Bohn, B.,Brauers, T., Dorn, H.P., Fuchs, H., Tillmann, R., Wahner, A., Saathoff, H.,Naumann, K.-H., M€ohler, O., Leisner, T., Müller, L., Reinnig, M.-C., Hoffmann, T.,Salo, K., Hallquist, M., Frosch, M., Bilde, M., Tritscher, T., Barmet, P., Praplan, A.P.,DeCarlo, P.F., Dommen, J., Pre

vot, A.S.H., Baltensperger, U., 2012b. Aging ofbiogenic secondary organic aerosol via gas-phase OH radical reactions. Proc.Natl. Acad. Sci. U. S. A. 109, 13503e13508.

Donahue, N.M., Chuang, W., Epstein, S.A., Kroll, J.H., Worsnop, D.R., Robinson, A.L.,Adams, P.J., Pandis, S.N., 2013. Why do organic aerosols exist? Understandingaerosol lifetimes using the two-dimensional volatility basis set. Environ. Chem.10, 151e157.

Dudhia, J., 2012. The WRF model: 2012 annual update. In: Presented at the 13th

Annual WRF Users' Workshop, 25e29 June 2012, Boulder, CO. http://www.mmm.ucar.edu/wrf/users/workshops/WS2012/ppts/1.1.pdf.

ENVIRON, 2011. User's Guide to the Comprehensive Air-quality Model with Ex-tensions (CAMx) Version 5.40. ENVIRON International Corporation, Novato, CA.http://www.camx.com/.

EPA, 2011a. Air Quality Modeling Final Rule Technical Support Document. Office ofAir Quality Planning and Standards, U.S. Environmental Protection Agency,Research Triangle Park, NC. http://www.epa.gov/crossstaterule/pdfs/AQModeling.pdf.

EPA, 2011b. Technical Support Document for the Final Transport Rule e EmissionsInventory Final Rule TSD. Office of Air Quality Planning and Standards, U.S.Environmental Protection Agency, Research Triangle Park, NC. http://www.epa.gov/airtransport/CSAPR/pdfs/EmissionsInventory.pdf.

Farina, S.C., Adams, P.J., Pandis, S.N., 2010. Modeling global secondary organicaerosol formation and processing with the volatility basis set: Implications foranthropogenic secondary organic aerosol. J. Geophys. Res. 115, D09202.

Fountoukis, C., Racherla, P.N., Denier van der Gon, H.A.C., Polymeneas, P.,Charalampidis, P.E., Pilinis, C., Wiedensohler, A., Dall'Osto, M., O'Dowd, C.,Pandis, S.N., 2011. Evaluation of a three-dimensional chemical transport model(PMCAMx) in the European domain during the EUCAARI May 2008 campaign.Atmos. Chem. Phys. 11, 10331e10347.

Guenther, A.B., Jiang, X., Heald, C.L., Sakulyanontvittaya, T., Duhl, T., Emmons, L.K.,Wang, X., 2012. The Model of Emissions of Gases and Aerosols from Natureversion 2.1 (MEGAN2.1): an extended and updated framework for modelingbiogenic emissions. Geosci. Model Dev. Discuss. 5, 1503e1560.

Hallquist, M., Wenger, J.C., Baltensperger, U., Rudich, Y., Simpson, D., Claeys, M.,Dommen, J., Donahue, N.M., George, C., Goldstein, A.H., Hamilton, J.F.,Herrmann, H., Hoffmann, T., Iinuma, Y., Jang, M., Jenkin, M.E., Jimenez, J.L.,Kiendler-Scharr, A., Maenhaut, W., McFiggans, G., Mentel, ThF., Monod, A.,Pr�evot, A.S.H., Seinfeld, J.H., Surratt, J.D., Szmigielski, R., Wildt, J., 2009. Theformation, properties and impact of secondary organic aerosol: current andemerging issues. Atmos. Chem. Phys. 9, 5155e5236.

Heald, C.L., Kroll, J.H., Jimenez, J.L., Docherty, K.S., DeCarlo, P.F., Aiken, A.C., Chen, Q.,Martin, S.T., Farmer, D.K., Artaxo, P., 2010. A simplified description of the evo-lution of organic aerosol composition in the atmosphere. Geophys. Res. Lett. 37,L08803.

Henry, K.M., Lohaus, T., Donahue, N.M., 2012. Organic aerosol yields from a-pineneoxidation: bridging the gap between first-generation yields and aging chem-istry. Environ. Sci. Technol. 46, 12347e12354.

Hildebrandt, L., Donahue, N.M., Pandis, S.N., 2009. High formation of secondaryorganic aerosol from the photo-oxidation of toluene. Atmos. Chem. Phys. 9,2973e2986.

Hodzic, A., Jimenez, J.L., Madronich, S., Canagaratna, M.R., DeCarlo, P.F., Kleinman, L.,Fast, J., 2010. Modeling organic aerosols in a megacity: potential contribution ofsemi-volatile and intermediate volatility primary organic compounds to sec-ondary organic aerosol formation. Atmos. Chem. Phys. 10, 5491e5514.

Jathar, S.H., Farina, S.C., Robinson, A.L., Adams, P.J., 2011. The influence of semi-volatile and reactive primary emissions on the abundance and properties ofglobal organic aerosol. Atmos. Chem. Phys. 11, 7727e7746.

Jimenez, J.L., Canagaratna, M.R., Donahue, N.M., Prevot, A.S.H., Zhang, Q., Kroll, J.H.,DeCarlo, P.F., Allan, J.D., Coe, H., Ng, N.L., Aiken, A.C., Docherty, K.S., Ulbrich, I.M.,Grieshop, A.P., Robinson, A.L., Duplissy, J., Smith, J.D., Wilson, K.R., Lanz, V.A.,

B. Koo et al. / Atmospheric Environment 95 (2014) 158e164164

Hueglin, C., Sun, Y.L., Tian, J., Laaksonen, A., Raatikainen, T., Rautiainen, J.,Vaattovaara, P., Ehn, M., Kulmala, M., Tomlinson, J.M., Collins, D.R., Cubison, M.J.,Dunlea, E.J., Huffman, J.A., Onasch, T.B., Alfarra, M.R., Williams, P.I., Bower, K.,Kondo, Y., Schneider, J., Drewnick, F., Borrmann, S., Weimer, S., Demerjian, K.,Salcedo, D., Cottrell, L., Griffin, R., Takami, A., Miyoshi, T., Hatakeyama, S.,Shimono, A., Sun, J.Y., Zhang, Y.M., Dzepina, K., Kimmel, J.R., Sueper, D.,Jayne, J.T., Herndon, S.C., Trimborn, A.M., Williams, L.R., Wood, E.C.,Middlebrook, A.M., Kolb, C.E., Baltensperger, U., Worsnop, D.R., 2009. Evolutionof organic aerosols in the atmosphere. Science 326, 1525e1529.

Jo, D.S., Park, R.J., Kim, M.J., Spracklen, D.V., 2013. Effects of chemical aging on globalsecondary organic aerosol using the volatility basis set approach. Atmos. En-viron. 81, 230e244.

Johnson, D., Utembe, S.R., Jenkin, M.E., Derwent, R.G., Hayman, G.D., Alfarra, M.R.,Coe, H., McFiggans, G., 2006. Simulating regional scale secondary organicaerosol formation during the TORCH 2003 campaign in the southern UK. Atmos.Chem. Phys. 6, 403e418.

Kroll, J.H., Donahue, N.M., Jimenez, J.L., Kessler, S.H., Canagaratna, M.R.,Wilson, K.R., Altieri, K.E., Mazzoleni, L.R., Wozniak, A.S., Bluhm, H.,Mysak, E.R., Smith, J.D., Kolb, C.E., Worsnop, D.R., 2011. Carbon oxidationstate as a metric for describing the chemistry of atmospheric organic aerosol.Nat. Chem. 3, 133e139.

Lane, T.E., Donahue, N.M., Pandis, S.N., 2008. Simulating secondary organic aerosolformation using the volatility basis-set approach in a chemical transport model.Atmos. Environ. 42, 7439e7451.

Lipsky, E.M., Robinson, A.L., 2006. Effects of dilution on fine particle mass andpartitioning of semivolatile organics in diesel exhaust and wood smoke. Envi-ron. Sci. Technol. 40, 155e162.

Malm, W.C., Schichtel, B.A., Pitchford, M.L., 2011. Uncertainties in PM2.5 gravimetricand speciation measurements and what we can learn from them. J. Air WasteManag. Assoc. 61, 1131e1149.

May, A.A., Presto, A.A., Hennigan, C.J., Nguyen, N.T., Gordon, T.D., Robinson, A.L.,2013a. Gas-particle partitioning of primary organic aerosol emissions: (1)gasoline vehicle exhaust. Atmos. Environ. 77, 128e139.

May, A.A., Presto, A.A., Hennigan, C.J., Nguyen, N.T., Gordon, T.D., Robinson, A.L.,2013b. Gas-particle partitioning of primary organic aerosol emissions: (2) dieselvehicles. Environ. Sci. Technol. 47, 8288e8296.

May, A.A., Levin, E.J.T., Hennigan, C.J., Riipinen, I., Lee, T., Collett Jr., J.L., Jimenez, J.L.,Kreidenweis, S.M., Robinson, A.L., 2013c. Gas-particle partitioning of primaryorganic aerosol emissions: 3. Biomass burning. J. Geophys. Res.118,11327e11338.

Murphy, B.N., Pandis, S.N., 2009. Simulating the formation of semivolatile primaryand secondary organic aerosol in a regional chemical transport model. Environ.Sci. Technol. 43, 4722e4728.

Murphy, B.N., Donahue, N.M., Fountoukis, C., Pandis, S.N., 2011. Simulating the ox-ygen content of ambient organic aerosol with the 2D volatility basis set. Atmos.Chem. Phys. 11, 7859e7873.

Murphy, B.N., Donahue, N.M., Fountoukis, C., Dall'Osto, M., O'Dowd, C., Kiendler-Scharr, A., Pandis, S.N., 2012. Functionalization and fragmentation duringambient organic aerosol aging: application of the 2-D volatility basis set to fieldstudies. Atmos. Chem. Phys. 12, 10797e10816.

Odum, J.R., Hoffmann, T., Bowman, F., Collins, D., Flagan, R.C., Seinfeld, J.H., 1996.Gas/particle partitioning and secondary organic aerosol yields. Environ. Sci.Technol. 30, 2580e2585.

Pankow, J.F., 1994a. An absorption-model of gas-particle partitioning of organic-compounds in the atmosphere. Atmos. Environ. 28, 185e188.

Pankow, J.F., 1994b. An absorption-model of the gas aerosol partitioning involved inthe formation of secondary organic aerosol. Atmos. Environ. 28, 189e193.

Presto, A.A., Donahue, N.M., 2006. Investigation of alpha-pinene plus ozone sec-ondary organic aerosol formation at low total aerosol mass. Environ. Sci.Technol. 40, 3536e3543.

Ranjan, M., Presto, A.A., May, A.A., Robinson, A.L., 2012. Temperature dependence ofgaseparticle partitioning of primary organic aerosol emissions from a smalldiesel engine. Aerosol Sci. Technol. 46, 13e21.

Reff, A., Bhave, P.V., Simon, H., Pace, T.G., Pouliot, G.A., Mobley, J.D., Houyoux, M.,2009. Emissions inventory of PM2.5 trace elements across the United States.Environ. Sci. Technol. 43, 5790e5796.

Robinson, A.L., Donahue, N.M., Shrivastava, M.K., Weitkamp, E.A., Sage, A.M.,Grieshop, A.P., Lane, T.E., Pierce, J.R., Pandis, S.N., 2007. Rethinking organicaerosols: semivolatile emissions and photochemical aging. Science 315,1259e1262.

Sheehan, P.E., Bowman, F.M., 2001. Estimated effects of temperature on secondaryorganic aerosol concentrations. Environ. Sci. Technol. 35, 2129e2135.

Shrivastava, M.K., Lipsky, E.M., Stanier, C.O., Robinson, A.L., 2006. Modeling semi-volatile organic aerosol mass emissions from combustion systems. Environ. Sci.Technol. 40, 2671e2677.

Shrivastava, M.K., Lane, T.E., Donahue, N.M., Pandis, S.N., Robinson, A.L., 2008. Ef-fects of gas-particle partitioning and aging of primary emissions on urban andregional organic aerosol concentrations. J. Geophys. Res. 113, D18301.

Shrivastava, M., Fast, J., Easter, R., Gustafson Jr., W.I., Zaveri, R.A., Jimenez, J.L.,Saide, P., Hodzic, A., 2011. Modeling organic aerosols in a megacity: comparisonof simple and complex representations of the volatility basis set approach.Atmos. Chem. Phys. 11, 6639e6662.

Tsimpidi, A.P., Karydis, V.A., Zavala, M., Lei, W., Molina, L., Ulbrich, I.M., Jimenez, J.L.,Pandis, S.N., 2010. Evaluation of the volatility basis-set approach for thesimulation of organic aerosol formation in the Mexico City metropolitan area.Atmos. Chem. Phys. 10, 525e546.

Yarwood, G., Rao, S., Yocke, M., Whitten, G.Z., 2005. Updates to the Carbon BondChemical Mechanism: CB05. Final Report Prepared for U.S. EnvironmentalProtection Agency. http://www.camx.com/publ/pdfs/CB05_Final_Report_120805.pdf.

Zhang, Q., Jimenez, J.L., Canagaratna, M.R., Allan, J.D., Coe, H., Ulbrich, I.,Alfarra, M.R., Takami, A., Middlebrook, A.M., Sun, Y.L., Dzepina, K., Dunlea, E.,Docherty, K., DeCarlo, P.F., Salcedo, D., Onasch, T., Jayne, J.T., Miyoshi, T.,Shimono, A., Hatakeyama, S., Takegawa, N., Kondo, Y., Schneider, J., Drewnick, F.,Borrmann, S., Weimer, S., Demerjian, K., Williams, P., Bower, K., Bahreini, R.,Cottrell, L., Griffin, R.J., Rautiainen, J., Sun, J.Y., Zhang, Y.M., Worsnop, D.R., 2007.Ubiquity and dominance of oxygenated species in organic aerosols inanthropogenically-influenced Northern Hemisphere midlatitudes. Geophys.Res. Lett. 34, L13801.

Zhang, Q.J., Beekmann, M., Drewnick, F., Freutel, F., Schneider, J., Crippa, M.,Prevot, A.S.H., Baltensperger, U., Poulain, L., Wiedensohler, A., Sciare, J., Gros, V.,Borbon, A., Colomb, A., Michoud, V., Doussin, J.-F., Denier van der Gon, H.A.C.,Haeffelin, M., Dupont, J.-C., Siour, G., Petetin, H., Bessagnet, B., Pandis, S.N.,Hodzic, A., Sanchez, O., Honor�e, C., Perrussel, O., 2013. Formation of organicaerosol in the Paris region during the MEGAPOLI summer campaign: evaluationof the volatility-basis-set approach within the CHIMERE model. Atmos. Chem.Phys. 13, 5767e5790.

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