Secondary Organic Aerosol in the UCI-CIT Airshed Model

Transcription

1 Secondary rganic Aerosol in the UCI-CIT Airshed Model The formation and evolution of particulate matter (PM) in the atmosphere presents one of the largest challenges to modeling air quality and climate in large-scale three-dimensional chemical transport models (CTM). However, decades of research demonstrate the potential of PM to have serious impacts on human health and Earths climate. Therefore, accurate forecasts of ambient PM and its response to changing emissions, meteorology and climate scenarios are critical to the development of models for economic, technological and industrial growth that must meet increasingly strict federal and state air quality and climate-related standards. Secondary organic aerosol (SA) forms when volatile organic species are oxidized in the atmosphere to form low-volatility products that preferentially partition to the particle phase. SA constitutes a significant fraction of total PM, 1 3 but is challenging to model for several reasons. These include, but are not limited to, the many thousands of individual species involved, the complex oxidation pathways that include gasphase routes as well as aqueous-phase oxidation and oligomerization within aerosol particles, and the role of kinetic limitations to particle-phase diffusion. Details of these processes are the focus of much current laboratory, computational, and field-based research. Therefore, CTMs must continually be updated to incorporate the latest scientific understanding of how SA forms and how it is processed in the atmosphere. Furthermore, the updates must be computationally feasible for large-scale models. The Computational Environmental Sciences (CES) Lab at the University of California, Irvine routinely employs the state-of-the-art University of California, Irvine California Institute of Technology (UCI-CIT) Airshed model to evaluate the effects of changing emissions scenarios on the South Coast Air Basin of California (SoCAB), and to investigate the impact of the latest findings of atmospheric researchers on criteria pollutants, including PM species, in a polluted urban environment. In this role, the UCI-CIT model regularly informs the decisions of regulatory agencies and policy makers, and helps direct future research efforts to systems with high potential impact. Integral to the UCI-CIT model is the CalTech Atmospheric Chemical Mechanism (CACM), 4 8 which includes a comprehensive treatment of SA, known as the Model to Predict the Multiphase Partitioning of rganics (MPMP). 5 As part of the UCI-CIT model, the CACM-MPMP module combines the latest scientific understanding of processes key to the formation and evolution of SA with advanced numerical algorithms for efficiently solving the complex equations related to aerosol physical processes. When it was introduced, CACM was the first chemical mechanism directly focused on the formation of SA species. Since its deployment in the UCI-CIT model, the CES lab has worked collaboratively with the developers of CACM at Rice University to ensure the treatment of SA in the UCI-CIT model incorporates the latest scientific understanding of SA-related processes in the atmosphere, and that model predictions mirror field measurements with ever increasing accuracy and precision. This document aims to describe the state-of-the-art SA module used in the current UCI-CIT model, and present several of the key findings that this advanced treatment of SA had made possible. UCI-CIT Model The UCI-CIT model is an Eulerian chemical transport model that solves simultaneously the advection/diffusion equation coupled with chemistry, emissions and deposition terms. The model includes state-of-the-art chem- 1

2 ical and aerosol mechanisms and applies advanced numerical algorithms for solving the non-linear system of highly-coupled differential equations involved in a parallel computational architecture. The model domain is divided horizontally into an 80 x 30 grid of 5 x 5 km cells, and vertically into five layers covering 1100 m above the surface. The domain covers the region shown in Figure 1, and includes range County, and parts of Los Angeles, Ventura, San Bernardino and Riverside Counties. Recently, the model has been updated to include emissions based on the 2012 Air Quality Management Plan provided by the South Coast Air Quality Management District 9 that includes current emissions and projected future emissions scenarios. CACM-MPMP SA Module Figure 1: Modeling domain resolution definition in the UCI-CIT model for the So- CAB. In large-scale models, gas-to-particle conversion is modeled typically as an equilibrium process. Two of the most successful and widely adopted equilibrium-based models for the gas-to-particle conversion of SA are the equilibrium partitioning model of Pankow and coworkers 10 and the volatility basis set (VBS) model In the equilibrium partitioning model, individual or representative intermediate- and low-volatility species are treated as being in equilibrium between the gas-phase and a liquid or liquid-like condensed-aerosol phase. Partitioning between the gas and condensed phases is calculated using thermodynamic properties of each SA species (e.g., vapor pressure or activity coefficient), and solved iteratively. The VBS approach treats SA partitioning similarly, as an equilibrium processes between gas and condensed phases. However, instead of considering the thermodynamic properties of individual or representative SA species to calculate partitioning, the VBS model groups all organic partitioning species into volatility bins, each with a specific fraction in the gas and condensed phases. Both approaches have advantages and limitations. The VBS model reduces the number of parameters required to describe complex SA systems, and provides a straightforward means to describe chemical aging of SA species by allowing an evolution along the volatility axis. However, this approach requires extensive parameterization based on laboratory and/or field measurements. n the other hand, because the equilibrium partitioning model is based on thermodynamic properties of real species, less parameterization is required, and even when these properties are not known for a particular SA species, they can often be estimated using existing structure-reactivity type techniques. However, due to the large number of species that contribute to SA in the atmosphere, a completely speciated SA model remains elusive. In order to overcome this limitation, models based on the equilibrium partitioning scheme often group SA species based on physical properties and/or chemical structure, with one set of thermodynamic properties representing an entire class of SA species. The CACM-MPMP SA module used in the UCI-CIT model is based on the equilibrium partitioning model and is one of the most advanced SA treatments currently available for regional atmospheric models. A description of this state-of-the-art SA treatment and several key advantages for regional SA modeling follow. Furthermore, a summary of some key model features is presented subsequently in Table 1 and described schematically in Figure 3. CACM: An SA-Focused Chemical Mechanism Accurately predicting SA formation in equilibrium partitioning based models requires a highly speciated gas-phase chemical mechanism that captures 2

3 key pathways in the oxidation of VCs that form SA species. CACM 6,7 was specifically designed to fill the need for such a comprehensive SA-focused gas-phase chemical mechanism. CACM s foundation is the Regional Atmospheric Chemistry Mechanism (RACM), 15 and the Statewide Air Pollution Research Center Mechanism (SAPRC-99). These chemical mechanisms are widely used in CTMs, 16 and primarily focus on modeling gas-phase pollutants and the chemistry of inorganic oxidants ( 3,, X ). To this, CACM adds a detailed VC oxidation scheme, which includes oxidation pathways for important classes of biogenic and anthropogenic VCs. Because of the large number of VCs present in the atmosphere, species in CACM are grouped based on structure, reactivity and experimentally determined potential to form SA, if available. The grouping scheme for primary VCs is also designed in harmony with data available in detailed emissions inventories provided by the South Coast Air Quality Management District (SCAQMD) for the SoCAB. Gas-phase processing of these grouped primary species then follows the dominant oxidation routes for a chosen surrogate species of average chemical structure. The implementation of CACM into the UCI-CIT model was the first time a comprehensive treatment of the gas-phase species and reactions involved in SA formation was included in a large-scale atmospheric model. 17 Since that time, the CES lab and collaborators at Rice University have worked continuously to update the CACM model with the latest findings of laboratory and field research related to the key species and mechanisms involved in the formation of SA partitioning species. For example, rates and relative product yields for aromatic oxidation reactions (an important SA precursor in urban areas) and biogenic oxidation reactions have been updated. This has resulted in better agreement with observations of gas-phase reaction products and SA yields for these important systems. 6 Currently, new chemical mechanisms related to formation of SA from the gas-phase oxidation of aromatics under varying X conditions, and from aqueous-phase organic oxidation reactions are being evaluated in the UCI-CIT model. If it is determined that this new chemistry represents an important source of SA in the SoCAB, these mechanisms will be permanently added to the model. At present, CACM includes 444 gas-phase reactions and 209 gas-phase chemical compounds, of which 39 are considered capable of SA-partitioning. SA Grouping Scheme In equilibrium-partitioning based models, the complexity with respect to the number of SA species is often reduced through lumping or grouping techniques, wherein lowand intermediate-volatility gas-phase species with similar properties (e.g., vapor pressure, Henry s Law constant, etc.) are treated as single SA species for the purpose of establishing the gas/particle-phase equilibrium. 7,18,19 Several considerations must be accounted for in the development of appropriate SA grouping schemes. First, the thermodynamic properties of individual SA species within a specific group must be similar enough that they can be accurately represented by a single surrogate compound. In addition, the characteristics of the modeling domain must be considered. Modeled SA is more representative of ambient SA when the most abundant individual SA-partitioning species in the domain are treated in separate groups, and act as the surrogate species for their respective groups. The SA grouping scheme in the UCI-CIT model was updated recently to ensure that the most abundant individual SA species in the modeled SoCAB were included in separate lumped SA groups, and that their physical constants were used to model their respective groups, without unduly increasing the diversity of individual thermodynamic parameters within SA groups. This increased the number of SA groups in the UCI-CIT model, and resulted in some redistribution between existing groups. Thus, predictions of total SA from the UCI-CIT model are more accurate, and the speciated composition of SA more representative of that found in the real atmosphere. The 39 gas-phase SA-partitioning compounds in 3

4 (b) (c) (d) (a) (g) (e) (h) (f) H H (i) (j) (k) (l) Figure 2: Representative species for each SA group in CACM-MPMP: (a) 1-methyl-1-hydroxy- 2-nitrato-4-isopropyl-cyclohexane, (b) 2-hydroxy-3-isopropyl-hexadial, (c) 1-methyl-2-nitrooxymethylnaphthalene, (d) 2,4-dimethyl-3-formyl-benzoic acid, (e) keto-propanoic acid, (f) 3,5-dimethyl-2-nitro- 4-hydroxy-benzoic acid, (g) 2-hydroxy-3-isopropyl-6-keto-heptanoic acid, (h) 3-hydroxy-2,4-dimethyl- 2,4-hexadiendial, (i) 4,5-dimethyl-6-keto-2,4-heptadienoic acid, (j) 11-hydroxy-8-hexadecanone, (k) 8- hexadecylnitrate, (l) 8-hydroxy-11-hexadecylnitrate CACM are grouped into 12 SA groups in the current UCI-CIT model. The surrogate structures for each of the 12 SA groups are shown in Figure 2. MPMP: A Fully Coupled Hydrophilic/Hydrophobic Model The oxidation of VCs leads to species that are both low volatility and highly polar, and thus can exist in both the aqueous / hydrophilic and organic / hydrophobic condensed phases. While previous SA models have included aqueous and organic phase SA formation, they were treated as separate systems with individual SA species partitioning to either the aqueous or the organic phase. This decoupled approach is known to lead to underestimates of SA formation as it fails to capture interactions between the two condensed aerosol phases. These interactions include shifts towards organic-phase SA under low relative humidity conditions, and similar shifts between phases related to varying aerosol ph. The CACM-MPMP model was the first to treat SA formation as a fully coupled aqueous and organic phase process, wherein every SA species can partition to both condensed phases. Through use of an advanced numerical algorithm, this fully coupled treatment was made computationally efficient enough to be included in large-scale models. In this manner, underpredictions of SA due to aqueous-organic phase interactions are eliminated, and 4

5 Table 1: UCI-CIT SA treatment summary. Chemical Mechanism CACM umber of Gas-Phase Species 136 umber of Gas-Phase Reactions 361 umber of SA-Forming Species 39 SA Module MPMP Model Type equilibrium partitioning o. of Lumped SA Species 12 SA Phase coupled aqueous/oragnic Vapor Pressures SIMPL.1 Activity Coefficients UIFAC Henry s Law Constants Suzuki et al. SA formation over the wide range of relative humidity (RH) and aerosol ph conditions present in the UCI-CIT modeling domain are captured accurately. Aerosol ph is primarily determined by inorganic aerosol species. In the UCI-CIT model, the CACM- MPMP SA module has been coupled with a comprehensive inorganic aerosol module, Simulating the Composition of Atmospheric Particles at Equilibrium 2 (SCAPE2), which has been updated to account for the interactions between aqueous-phase organic and inorganic ions, and to incorporate heterogeneous/multi-phase chemical reactions related to sea salt, which are important in a coastal area such as the SoCAB. 24 Thus, total PM along with important inorganic-sa interactions are captured in the UCI-CIT model. In many cases, the thermodynamic parameters required by equilibrium partitioning based models have not been determined experimentally for modeled SA species. Therefore, it is often necessary to use existing structure-reactivity type models to estimate vapor pressures, activity coefficients and Henry s Law constants. CACM-MPMP calculates Henry s Law constants according to the group contribution method of Suzuki et. al.. 25 Activity in both the aqueous and organic phases are determined using the UIFAC model. 26 Recently, the CACM-MPMP module has been updated to calculate vapor pressures using the SIMPL.1 method of Pankow and Asher. 27 SIMPL.1 is an empirical group contribution method, and was developed specifically for calculating vapor pressures of SA species in equilibrium partitioning models. It is ideally suited for this application, as it was parameterized and extensively evaluated using a large number of experimental vapor pressure measurements of compounds similar in structure to those shown in Figure 2, its predictions accurately cover a wide range of vapor pressures, and it includes group contribution terms for all the major functional groups present in the CACM-MPMP SA surrogate species. Select Results from Modeling Studies The UCI-CIT airshed model is used routinely to determine the impact of changing emissions scenarios on air quality in the SoCAB, and to evaluate the latest findings of atmospheric researchers for their potential importance in the real atmosphere. What follows are a few select results from these efforts as they relate to SA formation. 5

6 H H CACM A + B U + E V + S H + Z H + M F Q + P S + J + M B E + W W + L H + T X + C + D L + V F + I I + E C + R L P K + G U + E L + Z + D X P + Q MPMP SA Grouping 12 Lumped SA Species Thermodynamic Constants organic gas aqueous 136 species 361 reac:ons 39 gas- phase SA- forming species SIMPL.1 (vapor pressure) UIFAC (ac:vity coeff.) Suzuki et al. (Henry s Law) Figure 3: Schematic of SA treatment in the UCI-CIT model. Incremental SA Reactivity The incremental secondary organic aerosol reactivity (ISAR) of a species j is defined as the relative incremental change in secondary organic aerosol (SA) formed per relative incremental change in the amount of species j emitted. Carreras-Sospedra et al. 28 used the UCI- CIT airshed model coupled with CACM-MPMP to calculate spatially and temporally averaged ISAR values for the SoCAB. ISAR values were calculated for the lumped surrogate compounds considered by CACM: isoprene, low-yield monoterpenes, high-yield monoterpenes, high-yield aromatics, etc. Their work presents basin-wide ISAR values determined through regression analysis. In addition, ISAR values are reported at individual locations within the SoCAB. Modeled data are compared with ISAR values calculated using smog chamber data. Results indicate that long-chain alkanes present the highest ISAR. n the other hand, short-chain organics present the lowest ISAR. Partitioning Phase Preference for SA Chang et al. 29 used the UCI-CIT airshed model to understand the preferred partitioning behavior of SA species into aqueous or organic condensed phases. More specifically, they used statistical analyses of approximately 24,000 data values for each variable from the UCI-CIT model. Spatial and temporal distributions of fractions of SA residing in the aqueous phase (faq) in the SoCAB are presented. Typical values of faq within the basin near the surface range from 5 to 80%. Results show that the likelihood of large faq values is inversely proportional to the total SA loading. Analysis of various meteorological parameters indicates that large faq values are predicted because modeled aqueous-phase SA formation is less sensitive than that of organic-phase SA to atmospheric conditions that are not conducive to SA formation. There is a diurnal variation of 6

7 faq near the surface: It tends to be larger during daytime hours than during nighttime hours. Results also indicate that the largest faq values are simulated in layers above ground level at night. Their work demonstrates that one must consider SA in both organic and aqueous phases for proper regional and global SA budget estimation. SA from aphthalene xidation aphthalene is the simplest and most abundant polycyclic aromatic hydrocarbon (PAH) in California fuels, with concentrations of up to 2,600 mg L -1 in gasoline and 1,600 mg L -1 in diesel fuel. In this work, Cohan et al. 30 combine naphthalene emission factors for gasoline and diesel vehicles with an activity-based automobile inventory to characterize anthropogenic naphthalene emissions in the SoCAB. The UCI-CIT model is used to examine transport and chemical reaction losses of naphthalene in the SoCAB. Inclusion of naphthalene emissions from on-road gasoline and diesel vehicles was found to increase modeled SA growth by up to 10%. Their findings show that reductions of naphthalene from both gasoline and diesel fuels may be an effective means of reducing the emissions of an important SA-forming precursor to the atmosphere of large urban centers with characteristics similar to the SoCAB. SA Dynamics in the SoCAB As part of a study of the impact of emissions from ocean-going ships on ozone and particulate matter concentrations in the SoCAB, Vutukuru et al. 31 employed the UCI-CIT model for a base year (2002) and a future year (2020) and analyzed results from simulations of a threeday summer episode. The contribution of ship emissions to peak 1-h and 8-h ozone concentrations is predicted to be up to 29 and 24ppb, respectively, for the year Similarly, particulate nitrate and sulfate concentrations increase up to 12.8 and 1.7 µg m -3, respectively, in the basin when ship emissions are included. Maximum impacts are predicted to occur along the coasts of Ventura and Los Angeles and also at inland locations near Simi Valley. Future year simulations show substantial increase in impacts from ships due to expected growth in ship emissions. zone increases are as high as 59 ppb for landbased locations when estimates of ship emissions for 2020 are included. Similarly, particulate nitrate and sulfate increase up to 14 and 2.5 µg m -3. Although VC emissions from ships are small, part of the increase in PM is due to SA formed by the increase in 3. Their results show that control of ship emissions is important to mitigate air pollution, and that because of the coupled nature of the atmosphere, regional air quality models must include accurate treatment of SA, even when direct effects on SA are unexpected. Impact of Changing Ship Emissions in the SoCAB Vutukuru et al. 32 used the UCI-CIT model to explore the dynamics of SA formation in the SoCAB during an episode on 89 September Their results show that urban areas with major VC emission sites experience peaks in SA levels during morning hours. Downwind locations, such as Azusa and Claremont, experience sustained levels of high SA concentrations in comparison with coastal areas such as central Los Angeles and Long Beach. Concentrations of condensible organics are higher in inland locations compared to those in coastal locations because of high oxidation capacity and transport of pollutants. Furthermore, SA constitutes up to 30% of simulated organic particulate matter at inland locations, with maximum contributions occurring during afternoon hours. Anthropogenic sources contribute over 90% of simulated SA at most locations in the basin. xidation products of aromatic compounds from anthropogenic sources constitute over 70% of total simulated SA. Sensitivity runs indicate strong dependence of SA on VC emissions and temperature. verall, their model predictions are in qualitative agreement with contemporary observations in the SoCAB. 7

8 Table 2: UCI-CIT SA treatment features and advantages for air quality modeling. CACM MPMP highly speciated gas-phase chemical mechanism specifically developed for predicting SA precursors includes well established 3,, and X chemistry based on the RACM and SAPRC-99 gas-phase mechanisms uses detailed emissions inventories from the latest (2012) SCAQMD Air Quality Management Plan SA treatment based on equilibrium partitioning uses recently updated SA grouping scheme tailored to the SoCAB fully coupled aqueous/organic phase partitioning treatment for SA species linked to comprehensive inorganic aerosol module SCAPE2 Summary Accurate treatment of SA in regional air quality models is a key component of evaluating the effects of changing emissions scenarios on PM. The state-of-the-art SA module used in the UCI-CIT model and described here is designed to treat both aqueous- and organic-phase SA in a fully coupled manner according to an equilibrium partitioning scheme. This SA module is coupled to a gas-phase chemical mechanism specifically designed to capture major VC oxidation pathways that form SA species. In addition, this advanced SA treatment is continuously being updated to include the most representative species, reactions and thermodynamic parameters, thus ensuring that the UCI-CIT model continues to employ one of the most advanced treatments of SA available for large-scale models. The key features of the UCI-CIT SA treatment and their advantages for air quality modeling in the SoCAB are summarized in Table 2. 8

9 References Cited [1] M. Hallquist, J. C. Wenger, U. Baltensperger, Y. Rudich, D. Simpson, M. Claeys, J. Dommen,. M. Donahue, C. George, A. H. Goldstein, J. F. Hamilton, H. Herrmann, T. Hoffmann, Y. Iinuma, M. Jang, M. E. Jenkin, J. L. Jimenez, A. Kiendler-Scharr, W. Maenhaut, G. McFiggans, T. F. Mentel, A. Monod, A. S. H. Prvt, J. H. Seinfeld, J. D. Surratt, R. Szmigielski, and J. Wildt, The formation, properties and impact of secondary organic aerosol: current and emerging issues. Atmos. Chem. Phys. 9 (14), (2009). [2] J. H. Kroll and J. H. Seinfeld, Chemistry of secondary organic aerosol: Formation and evolution of low-volatility organics in the atmosphere. Atmos. Environ. 42 (16), (2008). [3] M. Kanakidou, J. H. Seinfeld, S.. Pandis, I. Barnes, F. J. Dentener, M. C. Facchini, R. Van Dingenen, B. Ervens, A. enes, C. J. ielsen, E. Swietlicki, J. P. Putaud, Y. Balkanski, S. Fuzzi, J. Horth, G. K. Moortgat, R. Winterhalter, C. E. L. Myhre, K. Tsigaridis, E. Vignati, E. G. Stephanou, and J. Wilson, rganic aerosol and global climate modelling: a review. Atmos. Chem. Phys. 5 (4), (2005). [4] R. J. Griffin, D. Dabdub, M. J. Kleeman, M. P. Fraser, G. R. Cass, and J. H. Seinfeld, Secondary organic aerosol - 3. Urban/regional scale model of size- and composition-resolved aerosols. J. Geophys. Res.- Atmos. 107, 4334 (2002). [5] R. J. Griffin, K. guyen, D. Dabdub, and J. H. Seinfeld, A coupled hydrophobic-hydrophilic model for predicting secondary organic aerosol formation. J. Atmos. Chem. 44 (2), (2003). [6] R. J. Griffin, D. Dabdub, and J. H. Seinfeld, Development and initial evaluation of a dynamic speciesresolved model for gas phase chemistry and size-resolved gas/particle partitioning associated with secondary organic aerosol formation. J. Geophys. Res. 110, D05304 (2005) DI: /2004JD [7] R. J. Griffin, D. Dabdub, and J. H. Seinfeld, Secondary organic aerosol 1. Atmospheric chemical mechanism for production of molecular constituents. J. Geophys. Res. 107, 4332 (2002). [8] B. K. Pun, R. J. Griffin, C. Seigneur, and J. H. Seinfeld, Secondary organic aerosol 2. Thermodynamic model for gas/particle partitioning of molecular constituents. J. Geophys. Res. 107, 4333 (2002). [9] S. C. A. Q. M. District, Final 2012 air quality management plan. [10] J. F. Pankow, An absorption model of the gas/aerosol partitioning involved in the formation of secondary organic aerosol. Atmos. Environ. 28 (2), (1994). [11]. M. Donahue, A. L. Robinson, C.. Stanier, and S.. Pandis, Coupled Partitioning, Dilution, and Chemical Aging of Semivolatile rganics. Environ. Sci. Technol. 40 (8), (2006). [12] A. L. Robinson,. M. Donahue, M. K. Shrivastava, E. A. Weitkamp, A. M. Sage, A. P. Grieshop, T. E. Lane, J. R. Pierce, and S.. Pandis, Rethinking organic aerosols: Semivolatile emissions and photochemical aging. Science. 315 (5816), (2007). [13] T. E. Lane,. M. Donahue, and S.. Pandis, Simulating secondary organic aerosol formation using the volatility basis-set approach in a chemical transport model. Atmos. Environ. 42 (32), (2008). 1

10 [14]. M. Donahue, S. A. Epstein, S.. Pandis, and A. L. Robinson, A two-dimensional volatility basis set: 1. rganic-aerosol mixing thermodynamics. Atmos. Chem. Phys. 11 (7), (2011). [15] W. R. Stockwell, F. Kirchner, M. Kuhn, and S. Seefeld, A new mechanism for regional atmospheric chemistry modeling. J. Geophys. Res. 102, (1997). [16] A. Russell and R. Dennis, ARST critical review of photochemical models and modeling. Atmos. Environ. 34 (12), (2000). [17] P. Jimenez, J. M. Baldasano, and D. Dabdub, Comparison of photochemical mechanisms for air quality modeling. Atmos. Environ. 37 (30), (2003). [18] B. Schell, I. J. Ackermann, H. Hass, F. S. Binkowski, and A. Ebel, Modeling the formation of secondary organic aerosol within a comprehensive air quality model system. J. Geophys. Res. 106, (2001). [19] R. A. Zaveri and L. K. Peters, A new lumped structure photochemical mechanism for large-scale applications. J. Geophys. Res. 104, (1999). [20] Z. Meng, D. Dabdub, and J. H. Seinfeld, Size-resolved and chemically resolved model of atmospheric aerosol dynamics. J. Geophys. Res. 103, (1998). [21] Y. P. Kim, J. H. Seinfeld, and P. Saxena, Atmospheric gas-aerosol equilibrium I. Thermodynamic model. Aerosol Sci. Technol. 19 (2), (1993). [22] Y. P. Kim, J. H. Seinfeld, and P. Saxena, Atmospheric gas-aerosol equilibrium II. Analysis of common approximations and activity coefficient calculation methods. Aerosol Sci. Technol. 19 (2), (1993). [23] Z. Meng, J. H. Seinfeld, P. Saxena, and Y. P. Kim, Atmospheric gas-aerosol equilibrium: IV. Thermodynamics of carbonates. Aerosol Sci. Technol. 23 (2), (1995). [24] E. M. Knipping and D. Dabdub, Impact of chlorine emissions from sea-salt aerosol on coastal urban ozone. Environ. Sci. Technol. 37 (2), (2003). [25] T. Suzuki, K. htaguchi, and K. Koide, Application of principal components analysis to calculate Henry s constant from molecular structure. Comput. Chem. 16 (1), (1992). [26] H. K. Hansen, P. Rasmussen, A. Fredenslund, M. Schiller, and J. Gmehling, Vapor-liquid equilibria by UIFAC group contribution. 5. Revision and extension. Ind. Eng. Chem. Res. 30 (10), (1991). [27] J. F. Pankow and W. E. Asher, SIMPL.1: a simple group contribution method for predicting vapor pressures and enthalpies of vaporization of multifunctional organic compounds. Atmos. Chem. Phys. 8 (10), (2008). [28] M. Carreras-Sospedra, R. J. Griffin, and D. Dabdub, Calculations of Incremental Secondary rganic Aerosol Reactivity. Environ. Sci. Technol. 39 (6), (2005). [29] W. L. Chang, R. J. Griffin, and D. Dabdub, Partitioning phase preference for secondary organic aerosol in an urban atmosphere. Proc. atl. Acad. Sci. U. S. A. 107 (15), (2010). 2

11 [30] A. Cohan, A. Eiguren-Fernandez, A. H. Miguel, and D. Dabdub, Secondary organic aerosol formation from naphthalene roadway emissions in the South Coast Air Basin of California. Int. J. Environ. Pollut. 52 (3), (2013). [31] S. Vutukuru, R. J. Griffin, and D. Dabdub, Simulation and analysis of secondary organic aerosol dynamics in the South Coast Air Basin of California. J. Geophys. Res. 111, D10S12 (2006) DI: /2005JD [32] S. Vutukuru and D. Dabdub, Modeling the effects of ship emissions on coastal air quality: A case study of southern California. Atmos. Environ. 42 (16), (2008). 3