# The formation, properties and impact of secondary organic aerosol: current and emerging issues

M. Hallquist, J. C. Wenger, Urs Baltensperger, Y. Rudich, D. Simpson, Marine Claeys, J. Dommen, N. M. Donahue, C. George, A. H. Goldstein, et al.

► To cite this version:

M. Hallquist, J. C. Wenger, Urs Baltensperger, Y. Rudich, D. Simpson, et al.. The formation, properties and impact of secondary organic aerosol: current and emerging issues. Atmospheric Chemistry and Physics, 2009, 9 (14), pp.5155-5236. hal-00431138

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# The formation, properties and impact of secondary organic aerosol: current and emerging issues

M. Hallquist¹, J. C. Wenger², U. Baltensperger³, Y. Rudich⁴, D. Simpson⁵,⁶, M. Claeys⁷, J. Dommen³,

N. M. Donahue⁸, C. George⁹,¹⁰, A. H. Goldstein¹¹, J. F. Hamilton¹², H. Herrmann¹³, T. Hoffmann¹⁴, Y. Iinuma¹³,

M. Jang¹⁵, M. E. Jenkin¹⁶, J. L. Jimenez¹⁷, A. Kiendler-Scharr¹⁸, W. Maenhaut¹⁹, G. McFiggans²⁰, Th. F. Mentel¹⁸,

A. Monod²¹, A. S. H. Prévôt³, J. H. Seinfeld²², J. D. Surratt²³, R. Szmigielski⁷, and J. Wildt¹⁸

¹Dept. of Chemistry, Atmospheric Science, University of Gothenburg, 412 96 Gothenburg, Sweden

²Dept. of Chemistry and Environmental Research Institute, University College Cork, Cork, Ireland

³Laboratory of Atmospheric Chemistry, Paul Scherrer Institut, 5232 Villigen PSI, Switzerland

⁴Dept. of Environmental Sciences, Weizmann Institute, Rehovot 76100, Israel

⁵EMEP MSC-W, Norwegian Meteorological Institute, P.B. 32 Blindern, 0313 Oslo, Norway

⁶Dept. of Radio and Space Science, Chalmers University of Technology, 41296, Gothenburg, Sweden

⁷Dept. of Pharmaceutical Sciences, University of Antwerp (Campus Drie Eiken), Universiteitsplein 1, 2610 Antwerp, Belgium

⁸Center for Atmospheric Particle Studies, Carnegie Mellon University, Pittsburgh PA 15213, USA

⁹Université de Lyon, Faculté de Chimie, 69003, France

¹⁰CNRS, UMR5256, IRCELYON, Institut de recherches sur la catalyse et l'environnement de Lyon, Villeurbanne, 69626, France

¹¹Dept. of Environmental Science, Policy and Management, University of California, Berkeley, CA, USA

¹²Dept. of Chemistry, University of York, Heslington, York, YO10 5DD, UK

¹³Leibniz-Institut für Troposphärenforschung, Permoserstrasse 15, 04318 Leipzig, Germany

¹⁴Johannes Gutenberg-Universität, Institut für Anorganische und Analytische Chemie, Duesbergweg 10–14, 55128 Mainz, Germany

¹⁵Dept. of Environmental Engineering Sciences, P.O. Box 116450, University of Florida, Gainesville, FL 32611-6450, USA

¹⁶Atmospheric Chemistry Services, Okehampton, Devon, EX20 1FB, UK

¹⁷Dept. of Chemistry & Biochemistry; and CIRES, University of Colorado, UCB 216, Boulder, CO 80309-0216, USA

¹⁸Institut für Chemie und Dynamik der Geosphäre, ICG, Forschungszentrum Jülich, 52425 Jülich, Germany

¹⁹Dept. of Analytical Chemistry, Institute for Nuclear Sciences, Ghent University, Proeftuinstraat 86, 9000 Ghent, Belgium

²⁰Centre for Atmospheric Sciences, School of Earth, Atmospheric & Environmental Sciences, University of Manchester, Simon Building, Manchester, M13 9PL, UK

²¹Université Aix-Marseille I, II et III, Case 29, Laboratoire Chimie Provence, UMR-CNRS 6264, 3 place Victor Hugo, 13331 Marseille Cedex 3, France

²²Depts. of Chemical Engineering and Environmental Science and Engineering, California Institute of Technology, Pasadena, CA 91125, USA

²³Dept. of Chemistry, California Institute of Technology, Pasadena, CA 91125, USA
**Abstract.** Secondary organic aerosol (SOA) accounts for a significant fraction of ambient tropospheric aerosol and a detailed knowledge of the formation, properties and trans- formation of SOA is therefore required to evaluate its im- pact on atmospheric processes, climate and human health. The chemical and physical processes associated with SOA formation are complex and varied, and, despite consider- able progress in recent years, a quantitative and predictive understanding of SOA formation does not exist and there- fore represents a major research challenge in atmospheric science. This review begins with an update on the current state of knowledge on the global SOA budget and is fol- lowed by an overview of the atmospheric degradation mech- anisms for SOA precursors, gas-particle partitioning theory and the analytical techniques used to determine the chem- ical composition of SOA. A survey of recent laboratory, field and modeling studies is also presented. The following topical and emerging issues are highlighted and discussed in detail: molecular characterization of biogenic SOA con- stituents, condensed phase reactions and oligomerization, the interaction of atmospheric organic components with sulfu- ric acid, the chemical and photochemical processing of or- ganics in the atmospheric aqueous phase, aerosol formation from real plant emissions, interaction of atmospheric organic components with water, thermodynamics and mixtures in at- mospheric models. Finally, the major challenges ahead in laboratory, field and modeling studies of SOA are discussed and recommendations for future research directions are pro- posed.

# 1 Introduction

Atmospheric aerosols, consisting of liquid or solid particles suspended in air, play a key role in many environmental pro- cesses. Aerosols scatter and absorb solar and terrestrial radi- ation, influence cloud formation and participate in heteroge- neous chemical reactions in the atmosphere, thereby affect- ing the abundance and distribution of atmospheric trace gases (Andreae and Crutzen, 1997; Haywood and Boucher, 2000). As a result, aerosols markedly affect the radiative balance in Earth's atmosphere and play a central role in climate (IPCC, 2007). Atmospheric aerosols also have an important impact on human health and it is now well established that expo- sure to ambient aerosols is associated with damaging effects on the respiratory and cardiovascular systems (Harrison and Yin, 2000; Davidson et al., 2005; Pope and Dockery, 2006). However, there are significant uncertainties in the true impact of atmospheric aerosols on climate and health because of a lack of knowledge on their sources, composition, properties

and mechanisms of formation (NRC, 2004; Pöschl, 2005; IPCC, 2007).

Atmospheric aerosols are formed from a wide variety of natural and anthropogenic sources. Primary particles are di- rectly emitted from sources such as biomass burning, com- bustion of fossil fuels, volcanic eruptions and wind-driven suspension of soil, mineral dust, sea salt and biological ma- terials. Secondary particles, however, are formed in the at- mosphere by gas-particle conversion processes such as nu- cleation, condensation and heterogeneous and multiphase chemical reactions. The conversion of inorganic gases such as sulfur dioxide, nitrogen dioxide and ammonia into partic- ulate phase sulfate, nitrate and ammonium is now fairly well understood. However, there is considerable uncertainty over the secondary organic aerosol (SOA)<fnref n="1" /> formed when the at- mospheric oxidation products of volatile organic compounds (VOCs) undergo gas-particle transfer. It is estimated that 10 000 to 100 000 different organic compounds have been measured in the atmosphere (Goldstein and Galbally, 2007). The complexity of the situation is compounded further by the fact that each VOC can undergo a number of atmospheric degradation processes to produce a range of oxidized prod- ucts, which may or may not contribute to SOA formation and growth. There is also an important difference between processes controlling particle number and processes control- ling particle mass; condensation of vapors (sulfuric and ni- tric acids, ammonia, and secondary organics) onto existing particles may dominate particle mass without necessarily in- fluencing particle number. Both number and mass are impor- tant to understand various aspects of the climate and health effects of atmospheric aerosols (Adams and Seinfeld, 2002; Oberdorster et al., 2005). Although clear progress has been made in recent years in identifying key biogenic and anthro- pogenic SOA precursors, significant gaps still remain in our scientific knowledge on the formation mechanisms, compo- sition and properties of SOA.

The objective of this paper is to review recent advances in our understanding of SOA. It builds upon a number of earlier reviews of organic aerosols in the atmosphere (Jacob, 2000; Jacobson et al., 2000; Turpin et al., 2000; Seinfeld and Pankow, 2003; Gelencsér, 2004; Kanakidou et al., 2005; Fuzzi et al., 2006; Sun and Ariya, 2006; Rudich et al., 2007) and complements the recent work of Kroll and Sein- field (2008) which focuses specifically on the chemistry of SOA formation. This review begins with an update on the current state of knowledge on the global SOA budget and is followed by an overview of the following topics related to the formation and characterization of SOA: gas-phase oxi- dation of SOA precursors, gas-particle partitioning and che- mical composition. Recent developments in laboratory, field and modeling studies are also presented. These sections pro- vide an effective foundation for the detailed discussions that follow on a range of current and emerging issues related to

<figure><img src="image_2.png" /></figure>

Correspondence to: J. C. Wenger
(j.wenger@ucc.ie)

<footnote n="1">¹A full list of abbreviations is provided in Sect. 7.</footnote>
the formation, composition, transformation and properties of SOA. The major challenges ahead are discussed and recommendations for future research directions are proposed.

## 2 Global SOA budget

Estimates of global SOA production have been made by two fundamentally different approaches. The traditional approach is a bottom-up estimate where known or inferred biogenic (most notably isoprene and terpenes) and/or anthropogenic VOC precursor fluxes are combined in global models with laboratory data from oxidation experiments leading to SOA formation in order to obtain a global organic aerosol field (Chung and Seinfeld, 2002; Kanakidou et al., 2005; Henze and Seinfeld, 2006; Henze et al., 2008). An alternative approach is a top-down inverse estimate based on constraining the eventual fate of known precursor emissions to infer the total SOA production rate (Goldstein and Galbally, 2007). These approaches give different results.

Bottom-up estimates give total biogenic SOA (BSOA) fluxes of 12–70 Tg/yr corresponding to biogenic secondary organic carbon (BSOC) fluxes of 9–50 TgC/yr for an organic matter to organic carbon ratio (OM/OC) of 1.4, which is typically assumed in many modeling studies (Kanakidou et al., 2005). The organic aerosol (OA) in bottom-up models shows a sharp vertical gradient, with much more present in the boundary layer than in the free troposphere (Heald et al., 2005). The sources of biogenic VOCs (BVOCs) are mainly derived from terrestrial ecosystems. However, there are also important emissions of BVOCs from the oceans, in particular of dimethylsulfide, which is oxidized to methanesulfonic acid aerosol (Kettle and Andreae, 2000). Other identified marine SOA components are dicarboxylic acids (Kawamura and Sakaguchi, 1999) and dimethyl- and diethylammonium salts (Facchini et al., 2008). Meskhidze and Nenes (2007) suggested that marine emissions from isoprene could also be a source of SOA. The latter is still an open question, although it has been estimated that the global production of SOA from marine isoprene is insignificant in comparison to terrestrial sources (Arnold et al., 2009). Models also include emissions of primary organic aerosol (POA) of about 35 TgC/yr (about 9 TgC/yr of anthropogenic POA and 25 TgC/yr of POA from open biomass burning (BB) such as forest fires; Bond et al., 2004) and a smaller contribution from anthropogenic SOA (ASOA) in the range 2–12 Tg/yr (~1.4–8.6 TgC/yr with OM/OC = 1.4; Henze et al., 2008). The total organic aerosol budget in bottom-up estimates thus ranges from 50 to 90 TgC/yr, clustering toward the low end.<fnref n="2" />

Recent top-down estimates using several different approaches lead to higher estimates for SOA, with a broad range from 140–910 TgC/yr (Goldstein and Galbally, 2007). These top-down estimates are an order of magnitude larger than the bottom-up estimates, and the extreme outer limits differ by roughly two orders of magnitude. SOA formation of 140–910 TgC/yr would require 11–70% of the entire mass of emitted VOCs (including isoprene, which represents 38% of the VOC budget) to be converted to the particle phase. The upper end of this estimate appears unrealistically high based on available data for SOA yields from chamber experiments (e.g., a few percent from isoprene). However, the difference in range of the top-down and bottom-up estimates clearly suggests that chamber oxidation experiments substantially underestimate total SOA production during the full course of the VOC oxidation process and is an issue that needs to be addressed.

Here, some new evidence based on recently measured aerosol composition will be employed to revise the top-down estimate from Goldstein and Galbally (2007) based on scaling from the sulfate budget, which is believed to provide the tightest constraint of the methods employed in that work. A “hybrid” approach, which combines this revised top-down estimate with bottom-up estimates from various other sources, will then be employed to estimate the BVOC contribution to the total OC budget. The various fluxes and uncertainty ranges used to develop this budget are presented in Table 1.

Particle mass spectrometry measurements on aircraft and at a number of ground-based monitoring locations reveal that most particles throughout the troposphere are mixtures of organics and sulfate ($SO_4^{2-}$), with the organic to sulfate ratio (organic/sulfate) ranging between 1:2 and 2:1 (Murphy et al., 2006; Zhang et al., 2007b). From this it can be inferred that organic/sulfate is roughly 1:1. Also, several techniques suggest that the OM/OC value typical of the background atmosphere is at least 2:1 (Turpin and Lim, 2001; Aiken et al., 2008). Thus, as the ratio of sulfur to sulfate (S/sulfate) is 1:3,

<footnote n="2">²A question arises on how to estimate the range of a summed quantity from the ranges of the summed components. For example if one wants to calculate the range of total SOA from the published estimated ranges for BSOA (9–50 TgC/yr) and ASOA (1.4–8.6 TgC/yr), one could add the extremes to come up with a total SOA estimate of (10.4–58.6 Tg/yr). However this procedure arti-</footnote>

<footnote>ficially inflates the uncertainty range. Thus here, and in later estimates, the ranges have been determined by first calculating the mean of the mid-range value (i.e. $(9+50)/2 = 29.5$ for BSOA and $(1.4+8.6)/2 = 5.0$ for ASOA) and a confidence interval of the difference between the range values (i.e., $50-9=41$ for BSOA and $8.6-1.4=7.2$ for ASOA). Uncertainty propagation rules are then applied to those values to estimate the mid-range value for the sum (i.e. $29.5+5.0=34.5$ for total SOA) and the size of the new confidence interval (i.e. $\sqrt{41^2+7.2^2}=41.6$ for total SOA). Finally the range for the summed quantity is calculated as the new mid-range value plus and minus the new confidence interval (i.e. $34.5-41.6/2 = 13.7$ and $34.5+41.6/2 = 55.3$ for total SOA) (D. Fahey, personal communication, 2008, NOAA, Boulder). Although the various range estimates which are being added in this way have been derived in different ways and may have different statistical properties, this method is preferable to other approaches. The fluxes presented here are also rounded to reflect the large uncertainties in these calculations.</footnote>
<table><caption>**Table 1.** Flux estimates (TgC/yr) for the different sources of organic aerosol based on the latest top-down approach. S1 and S2 represent two plausible scenarios, based on global constraints. See text for more details and a description of terms and abbreviations.</caption><thead><tr><th></th><th>Best estimate</th><th>Low limit</th><th>High limit</th><th>S1</th><th>S2</th><th>Modern OC</th><th>Fossil OC</th><th>HOA</th><th>OOA</th></tr></thead><tbody><tr><td>OC Top-down estimate</td><td>150</td><td>60</td><td>240</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Primary anthropogenic</td><td>5</td><td>2</td><td>8</td><td>2</td><td>5</td><td></td><td>5</td><td>5</td><td></td></tr><tr><td>Primary biomass burning</td><td>11</td><td>5</td><td>18</td><td>8</td><td>15</td><td>11</td><td></td><td>6</td><td>5</td></tr><tr><td>Oxidized low volatility anthropogenic</td><td>5</td><td>1</td><td>10</td><td>10</td><td>5</td><td></td><td>5</td><td></td><td>5</td></tr><tr><td>Oxidized low volatility biomass burning</td><td>14</td><td>1</td><td>26</td><td>20</td><td>15</td><td>14</td><td></td><td></td><td>14</td></tr><tr><td>Secondary anthropogenic</td><td>10</td><td>3</td><td>17</td><td>10</td><td>15</td><td></td><td>10</td><td></td><td>10</td></tr><tr><td>Secondary biomass burning</td><td>17</td><td>0</td><td>34</td><td>20</td><td>20</td><td>17</td><td></td><td></td><td>17</td></tr><tr><td>BSOC</td><td>88</td><td>0</td><td>180</td><td>10</td><td>150</td><td>88</td><td></td><td></td><td>88</td></tr><tr><td>Sum</td><td>150</td><td></td><td></td><td>80</td><td>225</td><td>130<br>(87%)</td><td>20<br>(13%)</td><td>11<br>(7%)</td><td>139<br>(93%)</td></tr></tbody></table>

the value for OC/S in particles throughout the troposphere is about 3:2, with a likely range from 3:1 to 3:4. The global sulfur budget is relatively well known, with most estimates between 50 and 110 TgS/yr (Barrie et al., 2001). Almost all sulfate is lost through particle deposition, so the sulfate particulate deposition flux is also between 50 and 110 TgS/yr. The same observations giving the value of 3:2 for the carbon to sulfur ratio (C/S) show that typical particles are internally mixed, and thus the mass ratio can be applied to the flux estimate without introducing significant biases. Consequently, the estimate of the global particulate OC deposition flux ranges between 60 and 240 TgC/yr. The best estimate flux is 150 TgC/yr.

The top-down estimates constrain the *total, net* organic aerosol flux, including primary and secondary emissions of both anthropogenic and biogenic material. To estimate the contribution to SOA from BVOC emissions we must thus subtract from the top-down estimate the contributions from other organic aerosol sources. These include primary emissions, anthropogenic SOA, and SOA formed from low-volatility vapors co-emitted with POA, for example from biomass burning. Additional constraints come from measurements of the overall oxidation state of the organic aerosol and radiocarbon constraints on the relative contribution of modern and fossil carbon sources.

Bottom-up POA emissions of about 35 TgC/yr include biomass and fossil-fuel burning (Bond et al., 2004). Here, it is estimated that primary anthropogenic emissions are between 5 and 15 TgC/yr and biomass-burning emissions are between 15 and 35 TgC/yr. However, recent work on primary emissions has shown that a large fraction of the compounds in concentrated plumes (tailpipes, near fire plumes, etc.) evaporates as the plume dilutes to ambient conditions (Shrivastava et al., 2007), with the vapors then oxidizing to form lower-volatility products (Robinson et al., 2007; Sage et al., 2008). Consequently, between half and two-thirds of the primary emissions in current inventories are likely to go

through a cycle of evaporation, oxidation, and recondensation as oxidized, lower-volatility products (Donahue et al., 2009). Our best estimate is that approximately 16 TgC/yr (9–23 TgC/yr) of the traditional POA remains permanently in the condensed phase while 19 TgC/yr (5–33 TgC/yr) undergo gas-phase oxidation before recondensing. The realization that emissions span a continuous range of volatility has created a debate about nomenclature. Semivolatile organic compound emissions with saturation concentrations below about 1000 µg m⁻³ are currently classified as non-volatile POA in most models. To avoid confusing policy makers, some have advocated calling this material “oxidized primary organic aerosol (OPOA)”. However, this label is easily mistaken for POA that is heterogeneously oxidized in the particle phase. Furthermore, much of the evaporation is thought to occur rapidly after emission, and most of the oxidation reactions are therefore expected to occur in the gas phase. Other than the low volatility of the emissions, these attributes are those of secondary organic aerosol; therefore, many advocate calling this material SOA as well.

Subtracting the low-volatility emissions from the top-down estimate, the global SOA formation from volatile precursors is about 115 TgC/yr, ranging between 25 and 210 TgC/yr. SOA from volatile precursors thus comprises approximately 70% of the OC mass, though with broad uncertainty. Furthermore, approximately 90% of the OC mass is likely to be in an oxidized form, based on the argument presented above.

Ambient aerosol mass spectrometry (AMS) measurements have revealed that OA mass spectra can be separated into several characteristic factors, which can significantly constrain this budget. Depending on the application and location, from two to six factors have been described. The original and simplest approach, which employs two factors to explain the large majority of the observed variance, will be used here. Ambient organic aerosol spectra can be separated into a relatively reduced component (Hydrocarbon-like Organic
Aerosol, HOA), and a relatively oxidized component (Oxidized Organic Aerosol, OOA) (Zhang et al., 2007b). HOA comprises only about 10% of the organic carbon (5% of OA) at background locations (Zhang et al., 2007b). However, direct emissions of partially oxidized POA, such as that produced from biomass burning (Aiken et al., 2008) may appear partially as OOA in the 2-factor solution. If we assume that slightly more than half (6 TgC/yr) of the biomass-burning POA appears as HOA, combined with 5 TgC/yr anthropogenic POA we estimate a global budget of 11 TgC/yr HOA and 139 TgC/yr OOA. This is consistent with the top-down flux estimate constrained by sulfate fluxes and also the ambient split between HOA and OOA.

Volatile anthropogenic precursors, typically substituted aromatics, are estimated to contribute between 1.4 and 8.6 TgC/yr to SOA, as discussed above (Henze et al., 2008). Additional anthropogenic precursors very likely result in several-fold more ASOA (Volkamer et al., 2006; de Gouw et al., 2008; Kleinman et al., 2008; Shrivastava et al., 2008), which is conservatively estimated here as a factor of 2, giving an estimated ASOA production rate of 10 TgC/yr, ranging from 3–17 TgC/yr. Along with the evaporation of POA emissions described above comes an uncertain flux of intermediate volatility material (Shrivastava et al., 2008). SOA formation from biomass-burning precursors is very uncertain at present and may only make a minor contribution (Capes et al., 2008) or be comparable to the biomass-burning POA (Grieshop et al., 2009). We estimate it here as 2/3 of the biomass-burning POA or 17 TgC/yr, ranging between 0 and 34 TgC/yr. These values combine to yield an estimated 27 TgC/yr SOA formation from non-BVOC volatile sources, ranging between 3 and 40 TgC/yr.

Subtraction of the estimated anthropogenic and biomass burning SOA from the estimate for total volatile SOA leads to a net BSOA production rate of about 90 TgC/yr, with a very wide potential range. The total flux estimates are summarized in Table 1. Net BSOA could be nearly zero, or it could be as much as 185 TgC/yr. There are two major differences from the bottom-up modeling estimates of SOA formation. First, those estimates appear to neglect significant non-BVOC sources of SOA. Second, even accounting for those sources, the top-down estimates suggest that modeling studies underestimate BSOA. The best estimate is that bottom-up inventories underestimate BSOA formation by about a factor of 2. We estimate that BVOC contribute approximately half of the total OC on a global basis, and that modern sources in total comprise about 80% of the OC.

The values presented here remain highly uncertain. This uncertainty is illustrated in Table 1 where two plausible scenarios, S1 and S2, based on global constraints are presented. Scenario S1 has a relatively low budget (80 TgC/yr) and only 12% of the global OA budget is attributed to BVOC oxidation. Instead, the large majority of OOA formation in this scenario comes from oxidation of lower volatility vapors emitted with combustion sources such as biomass burning

and internal combustion engines. On the other hand, scenario S2, with 225 TgC/yr total OC production, attributes 2/3 of the total to BVOC emissions. The final columns in Table 1 summarize both the modern versus fossil and HOA versus OOA attribution for the best-estimate scenario, but all three scenarios are broadly consistent within these constraints.

Figure 1 shows the overall VOC and SOA global flux estimates in a simplified manner following Goldstein and Galbally (2007). Organic non-methane carbon emissions are taken to be relatively well known at 1350 TgC/yr. In addition, global production of carbon monoxide from sources other than methane oxidation is also relatively well known, and even accounting for some uncertain production of CO2 through reactions that bypass CO, is thought to account for 300–500 TgC/yr. These numbers suggest that between 800 and 1000 TgC/yr are returned to Earth’s surface through dry and wet deposition of particles and vapors. This constitutes a large majority of the carbon emissions. It is exceedingly difficult to separate wet deposition of particles from wet scavenging of vapors, and dry deposition is even less well constrained. However, the estimates developed above based on relative carbon and sulfur deposition rates are entirely consistent with this global picture, and the total deposition flux is still reasonably constrained by the global mass balance. Ultimately, these mass-balance calculations leave the least certain number to be estimated as a difference. Goldstein and Galbally (2007) suggested that the most uncertain difference was due to SOA formation. However, vapor deposition is possibly the most uncertain term and could account for a majority of the global non-methane carbon flux. There are at least two significant ramifications to this. First, at least two of the most important parameters in the global VOC/SOA budget are very poorly constrained. Second, global models attempting to capture the dynamic behavior of organic aerosol will be very sensitive to these parameters; errors in the deposition calculation of both organic vapors and particles will create significant errors in the SOA production and burden.

This discussion has so far addressed the net SOA flux. As outlined in Sect. 5 of this paper, there is considerable evidence that vapor-particle transformation is dynamic and bidirectional: organic aerosol formation can be induced by gas-phase aging reactions that yield products with lower vapor pressures than their parents, yet on the other hand, the heterogeneous oxidation of organic particles can generate products with higher vapor pressures that evaporate from the particles. At present it is difficult to constrain the potential magnitude of these processes for estimations of SOA flux. For example, assuming an uptake coefficient of 1 for the hydroxyl (OH) radical on organic particles (Lambe et al., 2007; Rudich et al., 2007), the lifetime of organic particles towards heterogeneous oxidation is about 2 days (Molina et al., 2004; Robinson et al., 2006), i.e., the average organic compound within the particle has been oxidized at least once. Murphy et al. (2007) provide a field constraint on the rate of mass
<figure><img src="image_3.png" /><figcaption>**Fig. 1.** Global flux estimates of VOC and OA. Fluxes shown are described in the text and aerosol fluxes are summarized in Table 1.</figcaption></figure>

loss from oxygenated organic particles of tropospheric origin in the lower stratosphere. By analyzing the loss of organic mass from these particles, it is shown that particles larger than ~300 nm lose organic mass over a time scale of around 4–6 months in an environment with high levels of O₃ and OH. George et al. (2008) exposed ambient particles to OH for several equivalent days and found only a minor loss of particle mass by evaporation and only for equivalent exposures longer than 9 days. Further recent work (Capes et al., 2008; DeCarlo et al., 2008; Dunlea et al., 2008) found that a small loss of carbon was compensated by a gain of oxygen to keep the OA mass approximately constant (after accounting for dilution) during the evolution of pollution and biomass burning OA. Together these studies suggest that the evaporation of OA due to heterogeneous oxidation is unlikely to make a major contribution to the organic aerosol budget.

A further and important caveat to the above budget estimates is the emission rate of BVOC. Guenther et al. (1995) estimated the uncertainty in temperate regions to be a factor of three, and probably more in tropical areas. Although more recent estimates (Guenther et al., 2006), based upon a much larger number of field studies, have produced similar fluxes for isoprene as in the 1995 inventory, it is clearly essential that the global inputs of BVOC are established properly before the SOA budget can be fully understood. The constraints on the SOA budget will, however, certainly improve over the next few years due to the enhanced efforts in using and combining field measurement, laboratory and modeling work in this area.

# 3 Formation and characterization of SOA

## 3.1 Atmospheric degradation mechanisms of SOA precursors

The gas-phase degradation of VOCs is initiated by reaction with hydroxyl (OH) radicals, O₃, nitrate (NO₃) radicals or via photolysis. Under certain conditions in the marine atmosphere, chlorine atoms (Cl) may also initiate the oxidation of VOCs. The relative importance of these competing reactions depends on the structure of the VOC and on the ambient con-

ditions (Atkinson and Arey, 2003). The initial oxidation step leads to the generation of a set of organic products containing one or more polar oxygenated functional groups, such as aldehyde (—C(=O)H), ketone (—C(=O)—), alcohol (—OH), nitrate (—ONO₂), peroxyacyl nitrate (—C(=O)OONO₂), carboxylic acid (—C(=O)OH), hydroperoxide (—OOH) and percarboxylic acid (—C(=O)OOH) groups, which tend to make the products less volatile and more water soluble. Further oxidation may introduce additional functional groups, such that the “second-generation” set of products (and subsequent generations) may be of even lower volatility and higher solubility. However, the oxidation mechanisms also lead to fragmentation of the carbon chains to form lower molecular weight (MW) oxygenates (which tend to be more volatile), and the gas-phase oxidation, given enough time, would ultimately convert all the carbon in the emitted VOCs into CO₂. This is in stark contrast, therefore, with the situation for other secondary aerosol precursors, such as emitted SO₂ and NOₓ, for which the ultimate thermodynamically-favored products of the gas phase mechanisms (H₂SO₄ and HNO₃) are the forms that are most associated with the particle phase. SOA is therefore formed from the gas-to-particle transfer of partially oxidized organic material, occurring in competition with further oxidation in the gas phase.

As a result of the complexity of the emitted VOC mixture, and of the degradation chemistry, the atmosphere contains many thousands of structurally different organic oxygenates, which possess a wide range of properties (e.g., reactivity, photolability, volatility and aqueous solubility) and different propensities to undergo gas-to-particle transfer. The distribution of products formed, and the dependence of this distribution on ambient conditions such as NOₓ level and relative humidity (RH), is therefore a major factor in determining the influence that a given precursor VOC has on the formation of SOA. In addition, the degradation pathways of VOCs can have varying impacts on ozone formation and on the free-radical budget, and therefore also differing indirect effects on SOA formation, by virtue of the influence that these factors have on VOC oxidation rates in general.

The importance of emitted VOCs as SOA precursors is quite variable, with many emitted compounds generally believed to be very inefficient at generating SOA. However, certain classes of VOCs have long been identified as more likely to lead to SOA formation by virtue of their general high reactivity and types of oxidation product formed. Of particular significance are cyclic compounds, since the products of fragmentation (i.e., ring opening) processes often have the same (or similar) carbon number as the parent compound. Furthermore, in the cases of cycloalkenes, aromatic hydrocarbons and terpenes (the majority of which are cyclic), oxidation occurs predominantly by an addition mechanism, so that the first-generation products generally contain two (or more) polar functional groups. Consequently the oxidation of these classes of compounds is more likely to lead to the generation of low-volatility products than the oxidation of
similar sized VOCs in other classes. The characterization of SOA formation from the degradation of cyclic hydrocarbons has therefore been the focus of numerous chamber investigations over the years (e.g., Hoffmann et al., 1997; Odum et al., 1997; Griffin et al., 1999).

The more recent identification of oligomers and organosulfates in SOA formed in chambers and the atmosphere (Gao et al., 2004a, b, 2006; Kalberer et al., 2004; Iinuma et al., 2004, 2005, 2007a, b; Tolocka et al., 2004; Baltensperger et al., 2005; Samburova et al., 2005b; Dommen et al., 2006; Surratt et al., 2006, 2007a, b, 2008; Denkenberger et al., 2007; Gómez-González et al., 2008) has established that SOA formation from VOC degradation is not solely driven by the volatility of the gas-phase products formed, but is also influenced by the propensity of degradation products to undergo further reactions in the condensed phase, and how this depends on conditions such as water content, dissolved ions and pH. Condensed-phase reactions that increase the MW can sharply reduce the volatility distribution of the organics. This has substantially altered perceptions of which emitted VOCs can act as SOA precursors, to the point that SOA formation from the degradation of smaller VOCs can no longer be automatically disregarded; for example, even very volatile glyoxal (CHOCHO) can associate with other aldehydes and organic acids in particles to add to SOA mass (Kroll et al., 2005a; Volkamer et al., 2007) or can be sulfated after gem-diol formation (Surratt et al., 2007a, 2008; Gómez-González et al., 2008). Thus even acetylene (C₂H₂), the smallest hydrocarbon after methane, can produce significant SOA due to the high yield of glyoxal from its degradation (Volkamer et al., 2009). In theory the oxidation of methane can also contribute to SOA if e.g., the radical products such as CH₃O₂, CH₃O, or HCHO react to form species with much lower volatility.

### 3.1.1 Generation of atmospheric degradation mechanisms for VOCs

Only a very small fraction of the reactions occurring in the atmospheric degradation of VOCs have been studied experimentally. Only for the simplest hydrocarbons and oxygenates (e.g., methane, ethane, ethene, methanol, acetaldehyde, acetone and dimethyl ether) have the kinetics and products of the majority of the elementary reactions involved in their degradation to CO₂ and H₂O been investigated (Atkinson et al., 2006). Even for these compounds, there have been recent unexpected advances in understanding, and some uncertainties remain. For a number of other and more complex VOCs (including isoprene and selected terpenes and aromatic hydrocarbons), some important aspects of their degradation chemistry have been studied experimentally. This information provides a partial basis for defining their degradation chemistry, which can be supplemented by the inclusion of reactions and associated parameters which are inferred from the more detailed understanding of the chemistry of the

smaller compounds. For many other VOCs, little or no experimental information exists, and their degradation chemistry needs to be defined almost exclusively by inference. As a result, chemical mechanisms which treat the atmospheric degradation of a large number of significant emitted VOCs (e.g., Jenkin et al., 1997; Carter, 2000; Aumont et al., 2005) necessarily need to make the fundamental assumption that the kinetics and products of a large number of unstudied chemical reactions can be defined on the basis of the known reactions of a comparatively small number of similar chemical species. This is usually done by analogy and with the use of structure-reactivity relationships to provide relatively simple rules to guide mechanism construction (e.g., Jenkin et al., 2003; Saunders et al., 2003; Aumont et al., 2005). To varying extents, such mechanisms also incorporate strategic simplifications to limit their overall size, which would otherwise include as many as 10⁵–10⁶ reactions. The performance of these gas-phase mechanisms can be tested (and, if necessary, refined) using chamber data (e.g., Carter, 2000; Pinho et al., 2005, 2006, 2007). Such procedures have traditionally been focused on providing validated descriptions of ozone formation from the photooxidation of VOC/NOₓ mixtures, although the development of highly-instrumented chambers (see Table 2 in Sect. 4) has provided data to allow other aspects of the degradation mechanisms, including SOA formation pathways, to be examined and improved.

Mechanisms constructed in this way therefore reflect a number of generic features, which are reasonably well established for smaller VOCs, and used to infer the chemistry for larger VOCs and for their sequential degradation products. The most studied aspect of VOC degradation is the OH-initiated chemistry, for which a simplified schematic is shown in Fig. 2. This shows that the distribution of products formed is mainly governed by the reactions of the intermediate peroxy (RO₂) and oxy (RO) radicals, for which a number of competing reactions may exist. At high-NOₓ, the chemistry tends to be dominated by radical propagating chemistry, in which RO₂ is efficiently converted into RO via reaction with NO. The fate of the RO radical is strongly dependent on its structure, and therefore on the structure of the parent VOC. It also may depend on the prevailing temperature, if competitive reaction channels exist. As described in detail elsewhere (e.g., Atkinson, 2007), RO may typically react with O₂ to form a carbonyl product and HO₂, decompose through C-C bond scission (leading to a smaller carbonyl product and an organic radical), or isomerize through H-atom shift reactions (leading ultimately to a hydroxycarbonyl product and HO₂). The reactions of RO₂ radicals with NO also have terminating channels which form organic nitrate products (RONO₂), with the importance of these channels generally increasing as the size of the organic group (and therefore the parent VOC) increases. RO₂ radicals also react with NO₂ to form peroxynitrates (RO₂NO₂), although such species only tend to be stable at lower tropospheric temperatures for the specific case of peroxyacyl nitrates (PANs)
which are formed from peroxyacyl radicals of generic formula $RC(O)O_2$. As a result, VOC degradation at high-$NO_x$ levels tends to generate a product distribution that is dominated by carbonyls, hydroxycarbonyls, organic nitrates and PANs. It should also be noted that the reacting organic species may initially contain oxygenated functional groups, which are retained in the products.

At lower $NO_x$ levels, the reactions of $RO_2$ with $HO_2$ and with the $RO_2$ radical "pool" become competitive, leading to a progressive change in the product distribution with changing $NO_x$ level. As shown in Fig. 2, the reaction of simple $RO_2$ radicals (e.g., $CH_3O_2$) with $HO_2$ is known to be dominated by termination reactions to form hydroperoxide products (ROOH). The reactions with the $RO_2$ radical pool are partially propagating, to generate RO radicals (and therefore carbonyls and hydroxycarbonyls), and partially terminating to generate alcohol and carbonyl products. As a result, VOC degradation at very low-$NO_x$ levels tends to generate a product distribution which is dominated by the formation of hydroperoxides, carbonyls, hydroxycarbonyls and alcohols. Once again, it should be noted that the organic species may initially contain oxygenated functional groups, which are also retained in the products. In the case of peroxyacyl radicals, $RC(O)O_2$, the reactions with $HO_2$ and the $RO_2$ pool lead to the formation of percarboxylic acid ($RC(O)OOH$) and carboxylic acid ($RC(O)OH$) products. It should also be noted that the lifetime of the $RO_2$ radicals with respect to reactions with $HO_2$ and the $RO_2$ pool can be very long under atmospheric conditions (i.e., minutes). It is therefore possible that unimolecular rearrangements (e.g., Jorand et al., 2003) or heterogeneous aerosol reactions (Bonn et al., 2007) may also play a role for some peroxy radical classes.

The chemistry initiated by reaction with ozone and $NO_3$ radicals, and via direct photolysis, tends to feed into similar reaction mechanisms by virtue of the formation of OH, $RO_2$ and RO radicals. Reaction with ozone is significant for unsaturated compounds, and may be the dominant atmospheric fate in certain cases (e.g., for some monoterpenes and sesquiterpenes) (Atkinson and Arey, 2003). As described in detail elsewhere (Johnson and Marston, 2008), the ozone-initiated chemistry is generally believed to lead to the formation of Criegee intermediates, which typically either decompose to form OH and an organic fragment (which reacts with $O_2$ to form an $RO_2$ radical), or undergoes bimolecular reactions. In the latter case, the most prevalent reaction partner under atmospheric conditions is believed to be with water, to form carboxylic acids or hydroxyalkyl hydroperoxides. As a result of this complex chemistry, the product distribution (and therefore SOA-formation propensity) is sensitive not only to the presence of $NO_x$ (Donahue et al., 2005) and water (Bonn et al., 2002; Jonsson et al., 2006), but has also been shown to be influenced in chamber experiments by addition of different scavengers for both OH and Criegee biradicals. The use of different scavengers for OH can influence the $[RO_2]/[HO_2]$ ratio in the system (e.g., Keywood

<figure><img src="image_4.png" /><figcaption>**Fig. 2.** Simplified schematic of the OH-initiated degradation of generic VOCs to form first-generation products.</figcaption></figure>

et al., 2004; Jenkin, 2004; Docherty et al., 2005; Jonsson et al., 2008a), whereas addition of Criegee biradical scavengers such as carbonyls and acids have been shown to generate various high MW secondary ozonides and acyloxyalkyl hydroperoxides, respectively (e.g., Neeb et al., 1998; Tobias and Ziemann, 2000; Bonn et al., 2002).

### 3.1.2 Recent developments and uncertainties in degradation mechanisms for complex VOCs

The degradation chemistry of selected aromatic hydrocarbons and monoterpenes has in the past received considerable attention (e.g., Calvert et al., 2002; Atkinson and Arey, 2003), owing in part to their established importance as precursors to SOA formation. This effort has been maintained in recent years (e.g., Volkamer et al., 2001, 2002; Olariu et al., 2002; Alvarez et al., 2007; Bejan et al., 2007; Ma et al., 2007a, b; Metzger et al., 2008), leading to a progressive improvement in the understanding of some aspects of their degradation.

The degradation of aromatic hydrocarbons, although usually only initiated significantly by reaction with OH, can proceed via a number of different routes to generate a large variety of structurally complex ring-retaining and ring-opened products (e.g., Calvert et al., 2002). Even for well-established products (e.g., α-dicarbonyls and co-products formed from the major ring-opening channels), however, there is considerable variation in the reported yields, and for many of the studied systems, only ca. 50% or less of the aromatic loss has been accounted for by observed first-generation products. Particular areas where understanding is lacking include uncertainties in the mechanisms of the initial oxidation sequences to first-generation products, and limitations in the available information on the subsequent
chemistry of many of the classes of products known to be generated. The further degradation chemistry of first- and subsequent-generation products is believed to be particularly important in accounting for secondary radical generation and removal of NOx in chamber photooxidation experiments of aromatic/NOx systems (Wagner et al., 2003). In this respect, mechanisms constructed for aromatic hydrocarbons display evidence for a missing radical source in the system, but a simultaneous tendency towards over-production of ozone (e.g., Bloss et al., 2005a, b). This points to a clear gap in understanding, since these features cannot be readily reconciled within traditional understanding of VOC oxidation chemistry. Bloss et al. (2005b) outlined a number of speculative solutions, including the existence of novel isomerization reactions of complex RO2 radical intermediates to re-generate OH directly, and the possibility that NO2 might react with the SOA formed in the system to generate HONO. Although there was insufficient evidence to support either suggestion, the latter has subsequently received some indirect support from the recognition that photosensitized NO2-to-HONO conversion can occur on organic surfaces (e.g., George et al., 2005; Stemmler et al., 2006). More recently, Metzger et al. (2008) have proposed that the light-induced conversion of NO2 to HONO occurs at the chamber walls rather than on the SOA itself.

Whereas the uncertainties in aromatic degradation are, to some extent, generic (i.e., an advance in mechanistic understanding is likely to be applicable to aromatics in general), understanding of the degradation of terpenes and related biogenics is further hampered by the enormous variety in their structure and reactivity (atmospheric lifetimes range from minutes to days). In addition to this, three initial reactions, with OH, O3 and NO3, must be considered in their atmospheric degradation. Under the conditions typically employed in chamber hydrocarbon/NOx photooxidation experiments, the initial reaction with O(3P) atoms can also be significant (Pinho et al., 2007). Although kinetic data are available for a wide variety of terpenes (Atkinson and Arey, 2003), experimental information on their degradation pathways is much more limited. Selected product yields (usually for first-generation carbonyls) are available for a number of species, but the most detailed information has been reported for the monoterpenes α- and β-pinene. Even for these species, however, it is clear that the distribution of first-generation products, and the elementary processes leading to their formation, remain far from fully characterized, and that experimental information on the further degradation of first-generation products is very sparse indeed (e.g., Pinho et al., 2007).

Detailed chemical mechanisms for such species therefore necessarily contain a substantial proportion of inferred reactions and estimated parameters. In recent years, theoretical methods have been applied with some success to elucidate aspects of the degradation of some complex VOCs, in particular α-pinene (e.g., Peeters et al., 2001; Fantechi et al., 2002;

Vereecken et al., 2004, 2007). Such methods invariably identify exotic reaction pathways which are not predicted by the extrapolation methods which are based on relatively simple structure-reactivity relationships as outlined above. Although such alternative pathways have been shown to have a reasonably subtle influence on simulations of oxidant formation (Pinho et al., 2007), they have a notable influence on the precise structures of the products formed, and therefore potentially have a much greater effect on the perceived propensity of product distribution to contribute to SOA formation (Vereecken et al., 2007). It is also clear that SOA formation is often confined to a relatively small fraction of the carbon from any given precursor (Donahue et al., 2009). Thus, it is not obvious that mechanisms derived using simple structure-reactivity methods are always appropriate to describe SOA formation, where more exotic, small yield products may play a disproportionate role in SOA production.

From the above discussion, it is clear that, despite considerable investigation, the elementary processes involved in the degradation of complex VOCs, such as aromatic hydrocarbons and terpenes, remain very sparsely characterized. The majority of information relates to the initial oxidation sequences to form first-generation products, with the further degradation of established first-generation products often not characterized at all. It is also becoming clear that chamber datasets for the oxidation of the product compounds (and possibly their degradation products) would be valuable in assessing the performance of detailed degradation mechanisms, and for gaining insights into chemical processes occurring on timescales longer than those addressed by conventional chamber experiments such as those encountered in ambient conditions. This is particularly important for large complex emitted VOCs, which are typically degraded via a large number of sequential (and parallel) steps. Finally, it should be recognized that the chemical mechanisms for the atmospheric degradation of other potential SOA precursors, such as sesquiterpenes, long-chain alkanes and oxygenates, are also in need of improvement.

## 3.2 Gas-particle partitioning theory

The phase partitioning of SOA is generally described using the theoretical foundations on organic aerosol phase partitioning developed by Pankow in the 1990s (Pankow, 1994) and extended by Odum to SOA formation (Odum et al., 1996). The fundamental concept is that SOA comprises a mixture of semivolatile organic compounds that partition between the gas and particle phases. Partitioning of each compound is described by an equilibrium partitioning coefficient $K_{p,i}$ (m$^3$ µg$^{-1}$), or equivalently (Donahue et al., 2006) its inverse, the saturation vapor concentration, $C_i^*$ (µg m$^{-3}$):

$$ \frac{C_i^p}{C_i^g} = K_{p,i} C_{OA} = \frac{C_{OA}}{C_i^*} \qquad (1) $$
where $C_i^g$ is the mass concentration of species i per unit volume of air ($\mu g m^{-3}$) in the gas phase, $C_i^p$ is the mass concentration per unit volume of air ($\mu g m^{-3}$) in the particulate phase, and $C_{OA}$ is the mass concentration per unit volume of air ($\mu g m^{-3}$) of the total absorbing particle phase. Both $K_{p,i}$ and $C_i^*$ can in theory be derived from the saturation vapor pressure of the pure compound if its activity coefficient in the absorbing phase can be determined (Bowman and Karamelegos, 2002; Chang and Pankow, 2006), although Donahue et al. (2006) have suggested that this formulation can be simplified with a modified version of Raoult's law. $C_{OA}$ refers only to the portion of the particulate matter participating in absorptive partitioning (an organic aerosol into which semivolatile organics can partition and possibly the aqueous portion of the particles in the case of highly water-soluble organics). Note that as long as some absorbing mass is present, some fraction of a given semivolatile compound will partition into the particle phase, even if its gas-phase concentration is below its saturation concentration, $C_i^*$. Equation (1) can be used to obtain the fraction $F_i$ of a semivolatile compound in the particle phase:

$$F_i = \frac{C_i^p}{C_i^p + C_i^g} = \frac{C_{OA} K_{p,i}}{1 + C_{OA} K_{p,i}} = \frac{1}{1 + C_i^*/C_{OA}} \quad (2)$$

Thus as the amount of absorbing material ($C_{OA}$) increases, compounds of greater volatility (larger $C_i^*$, smaller $K_{p,i}$) will increasingly partition into the particle phase. When $C_i^* = C_{OA}$ half of the semivolatile mass of species i resides in the particle phase. If $C_{OA} \gg C_i^*$, essentially all of the semivolatile species i is in the particle phase.

In the first basic model of SOA formation in smog chambers using partitioning theory, Odum et al. (1996) represented the process by the generation of n semi- (or non-) volatile products, e.g., P₁, P₂, ... Pₙ, and showed that the SOA yield, or mass fraction, $F_{OA}$, defined as the mass of SOA produced ($\Delta M_{OA}$) when a certain mass of a precursor hydrocarbon ($\Delta M_{HC}$) is oxidized can be derived from Eq. (1) and expressed as:

$$F_{OA} = \frac{\Delta M_{OA}}{\Delta M_{HC}} = C_{OA} \sum_{i} \frac{\alpha_{i} K_{p,i}}{1 + C_{OA} K_{p,i}} \equiv \sum_{i} \frac{\alpha_{i}}{1 + C_{i}^{*} / C_{OA}} \quad (3)$$

where $\alpha_i$ is the mass-based stoichiometric yield (not stoichiometric coefficient) of compound i. The “volatility distribution” of the oxidation products is represented by the product yields ($\alpha_i$) and partitioning coefficients ($K_{p,i}$). The two-product ($n=2$) version of Eq. (3) was used by Odum et al. (1996) because adding more products did not improve the fit to the yields obtained from the chamber studies. As a result, the two-product model has been used as the standard means of representing laboratory SOA yield data in many experimental and modeling studies (Seinfeld and Pankow, 2003; Kanakidou et al., 2005). Recently, Chan et al. (2007) have presented a framework to extend the simple product model to take into account the kinetics of both

gas- and aerosol-phase processes. In their framework the initial oxidation products may react further to generate second-generation products, semivolatile aerosol products may react to generate non-volatile products, etc. This framework has been further discussed in Kroll and Seinfeld (2008).

### 3.2.1 The volatility basis set (VBS) approach

With increasing understanding of the nature and complexity of SOA formation, the limitations of representing laboratory data with a two-product model are now evident. There are two major issues: the wide range of $C_{OA}$ in the atmosphere and the ongoing oxidation of semivolatile organics in both the gas and particle phases. Donahue and co-workers (Donahue et al., 2006; Presto and Donahue, 2006; Pathak et al., 2007) have proposed the use of a “volatility basis set” (VBS) to address these issues. The VBS consists of a group of lumped compounds with fixed $C^*$ values, comprising up to 9 “bins” separated by one order of magnitude each in $C^*$ at 300 K. Using the VBS, different SOA-forming reactions can be mapped onto the same set of bins over the range of organic aerosol mass concentration typical of ambient conditions (~0.1–100 $\mu g m^{-3}$) while maintaining mass balance for more volatile co-products as well. Aging reactions within the VBS can be added easily if the kinetics and volatility distribution of the products can be measured or estimated. Figure 3 shows how experimental SOA yield data from the $\alpha$-pinene + ozone reaction can be fitted to the VBS, given a mass balance constraint that the total mass of gaseous and particulate phase products is about 1.4 times the mass of $\alpha$-pinene reacted (the mass gain is from added oxygen). The inset to Fig. 3 shows the partitioning when 26 $\mu g m^{-3}$ of $\alpha$-pinene is oxidized to give 1 $\mu g m^{-3}$ of SOA, making clear that under typical ambient conditions most of the first-generation products are vapors of a wide range of volatilities.

### 3.2.2 Unresolved issues with partitioning

Several factors complicate the interpretation of partitioning in both experimental work and in the atmosphere. Many of these are addressed in more detail in Sect. 5 of this paper, but here the factors affecting the interpretation of experimental data or implementation in models are highlighted.

1. **Complexity.** The ensemble of gaseous and particulate phase species involved in SOA formation has enormous complexity, and the identity and properties of only a small percentage of these are known. The compounds relevant to SOA formation are sometimes minor products with yields of only a few percent. These are especially vulnerable to experimental error, and difficult to parameterize in simplified chemical mechanisms.

2. **Interpretation of $C_{OA}$**. It is not well established what portion of the total organic aerosol mass should be used
to establish the mass of the condensing phase, $C_{OA}$. It is not certain whether primary organic aerosol mass should be included (Song et al., 2007), or how amor- phous or possibly solid fractions affect the OC avail- able for mixing and absorption (e.g., if some of the or- ganic material in a particle is surrounded by a dry am- monium sulfate shell). The role of water-uptake and of inorganics in $C_{OA}$ is also difficult to quantify. This is addressed further in Sect. 5.6.

3. *Temperature.* It is typically assumed that $C^*$ varies with temperature according to the Clausius-Clapeyron equa- tion (Chung and Seinfeld, 2002; Takekawa et al., 2003), though the appropriate values for enthalpies of vapo- rization, $\Delta H_v$, are highly uncertain (Hallquist et al., 1997; Bilde and Pandis, 2001; Chung and Seinfeld, 2002; Pathak et al., 2007; Stanier et al., 2007; Saathoff et al., 2009). As discussed in Donahue et al. (2006), measurements of $\Delta H_v$ for specific low-volatility com- pounds range between 40 and 110 kJ/mol, whereas data for aggregate organic aerosol suggest an overall temper- ature dependence equivalent to $\Delta H_v$ of ca. 40 kJ/mol or less. Donahue et al. (2006) showed that this apparent discrepancy can be reconciled, concluding that models should not confuse two possible approaches; models with few semivolatile components should use artificially low $\Delta H_v$ values, while models with numerous compo- nents covering a wide range of values for $C^*$ should use more realistic $\Delta H_v$ values.

4. *Thermodynamics and solution theory.* Vapor pressures, molar activity coefficients and the mean MW of the con- densing phase are all difficult properties to measure or estimate, even for those compounds whose identity is known (e.g., Clegg et al., 2008a, b). Difficulties in- crease when considering the role of relative humidity on organic partitioning (Chang and Pankow, 2006). The thermodynamics of mixtures in a laboratory experiment and in the atmosphere may also differ, as atmospheric mixtures are presumably much more diverse.

5. *Experimental artifacts and difficulties.* Aerosols (and vapors) are lost to chamber surfaces in laboratory expe- riments, complicating the mass balance. Further, SOA is frequently measured as an *apparent volume* via the particle size distribution, meaning that the condensed- phase *density* must also be determined or assumed.

6. *Chemically dynamic system.* All of the reaction pro- ducts important to SOA formation are subject to ongo- ing chemical degradation. Consequently, the yields ($\alpha_i$) may not be constant in time during an experiment, or relevant to the atmosphere. Gas-phase products from VOC oxidation depend strongly on ambient composi- tion, most notably through the fate of organic peroxy radicals ($RO_2$), which can react with nitrogen oxides

<figure><img src="image_5.png" /><figcaption>**Fig. 3.** Volatility Basis Set (VBS) distribution of products from the $\alpha$-pinene + ozone reaction. The x axis represents both the aerosol mass concentration $C_{OA}$ and the saturation concentration $C^*$ of the products, which are both expressed in $\mu g m^{-3}$. Product $C^*$ values are shown as green bars, normalized by the $\alpha$-pinene, shown in red. The fractional mass yield $F_{OA}$ for a given $C_{OA}$ is shown as a green curve, passing through data shown in gray and discussed in Presto and Donahue (2006). More recent yield data from Shilling et al. (2009) are somewhat higher. The inset shows the partitioning in each VBS bin when $26 \mu g m^{-3}$ $\alpha$-pinene is oxidized to give $1 \mu g m^{-3}$ SOA, corresponding to a mass yield of around 4%.</figcaption></figure>

(NO, $NO_2$), hydroperoxy radicals ($RO_2$), or other $RO_2$. This is commonly described as a VOC:$NO_x$ depen- dence. Also, oxygenated organics are often susceptible to photolysis by UV light (Kroll et al., 2005a; Presto et al., 2005a; Zhang et al., 2006). Finally, the semivolatile oxidation products may be formed from first- or higher- generation reactions, and the products themselves may react further in the gas or particle phases to yield com- pounds of either less (in the case of addition of more functional groups) or greater (in the case in which the carbon backbone of the molecule is cleaved) volatility.

7. *Displaced equilibria.* A serious limitation of currently applied partitioning approaches is that reactions of ei- ther gas or particle-phase compounds will displace the equilibrium given by Eqs. (1) and (2). In this case, the ratio $F_i$ is not just a function of $K_{p,i}$ (or $C_i^*$) and $C_{OA}$, but also of the rate at which $C_i^g$ or $C_i^p$ are reduced dur- ing reactions. Both condensed-phase and heterogeneous reactions may play a role (e.g., Chan et al., 2007; Kroll and Seinfeld, 2008) as outlined in Sect. 5.

## 3.3 Measurement of SOA chemical composition

The determination of SOA composition covers a wide range of analytical techniques and a number of reviews have been published in recent years (McMurry, 2000; Hoffmann and Warnke, 2007; Rudich et al., 2007). The aim of this sec- tion is to cover some of the most recent advances in SOA
analysis. One of the main barriers to a complete characterization of OA and SOA is the sheer number of individual species present. Goldstein and Galbally (2007) showed that for alkanes with 10 carbons there are about 100 possible isomers, increasing to well over 1 million $C_{10}$ organic species when all typical heteroatoms are included. Many of these species may be present in the atmosphere, and taking this into account, SOA quantification and chemical composition analysis tools generally fall into three categories depending on the degree of characterization required; indirect methods, off-line and on-line techniques. Indirect methods quantify total SOA from the difference between the measured total OA and estimated POA. Off-line high complexity techniques, e.g., gas chromatography/mass spectrometry (GC/MS), liquid chromatography/MS (LC/MS), nuclear magnetic resonance (NMR) and Fourier transform infrared (FTIR) spectroscopy, provide detailed information on individual chemical species or functional groups in SOA but generally require large amounts of sample, resulting in low time resolution (hours to days) and low size resolution. On-line techniques (e.g., aerosol mass spectrometry, AMS) usually provide less specific information on composition, i.e., some level of chemical characterization without details on individual species, but have the advantage of fast acquisition times, providing near real-time data.

Figure 4 highlights how some of the most important current field-deployable techniques compare for three important characteristics: completeness, chemical resolution, and time/size resolution. As described above, techniques that provide molecular speciation, represented by GC/MS in the figure, can only do so for a small mass fraction of the OA (of the order of 10%) present in ambient aerosol. Time resolutions are typically of many hours although recently a 1 h GC/MS instrument has been demonstrated (Williams et al., 2006, 2007), but without any size resolution. Improvements in this type of speciated techniques are highly desirable and are being actively pursued (e.g. Goldstein et al., 2008) but it is very unlikely that in the foreseeable future a technique or combination of techniques can provide the fully speciated and quantitative composition of all the OA with a time resolution of minutes or better and some size resolution. Thermal-optical EC/OC analyzers can quantify total OC with 1 h time resolution but without size-resolution. The PILS-WSOC technique, which involves the use of a particle-into-liquid sampler (PILS) combined with analysis for water-soluble organic compounds (WSOC), can be used to quantify water-soluble OC with a time resolution of minutes and without size resolution (and also water-insoluble OC by difference from e.g. a thermal-optical instrument, but then limited to 1 h time resolution). A family of complementary techniques (AMS, FTIR and NMR spectroscopy) attempts to analyze most of the OA mass while providing resolution of some chemical classes or functional groups. Of these techniques AMS has much higher time and size resolution (e.g. DeCarlo et al., 2008) and can provide elemental composi-

<figure><img src="image_6.png" /><figcaption>**Fig. 4.** Three-dimensional representation of some techniques currently used for the analysis of the organic content of aerosol, highlighting their complementary nature. Definition of the acronyms is provided in the text and in the list of abbreviations.</figcaption></figure>

tion (Aiken et al., 2008), but the chemical characterization provided by FTIR and NMR spectroscopy is more directly related to functional groups (e.g. Maria et al., 2003; Decesari et al., 2007). Thus a perfect field instrument with all the ideal characteristics does not exist, and at present a combination of techniques is required for a more complete characterization of OA and SOA.

### 3.3.1 Off-line high complexity SOA measurements

Generally the detailed analysis of SOA is performed in the laboratory using aerosol samples collected onto filters followed by extraction of the organic compounds using techniques such as solvent extraction (Cheng and Li, 2004), supercritical fluid extraction (Chiappini et al., 2006) or thermal desorption (Greaves et al., 1985; Veltkamp et al., 1996) to release the semivolatile species. A range of solvents and pre-treatments can be used, such as derivatization, to increase the range of species analyzed and often the specific solvent can be used as an empirical definition of the species present e.g., “water-soluble organic compounds” (WSOC). Thermal desorption has gained increasing popularity over the last few years for the measurement of semivolatile, thermally stable organic aerosol components and can be used without any sample preparation when combined with high resolution chromatographic techniques (Hays and Lavrich, 2007).

First-generation VOC oxidation products are generally polar substances containing hydroxyl, carboxyl, keto and/or aldehyde groups. Further reaction of first-generation photooxidation products through oxidation and sulfation of
hydroxyl and/or keto or aldehyde groups renders these pro-
ducts even more polar. The presence of hydroxyl, carboxyl,
keto, aldehyde, sulfate and nitrooxy groups in SOA con-
stituents requires that suitable analytical methods are em-
ployed and developed for their detection and characteriza-
tion at the molecular level. The most commonly employed
analytical techniques for the molecular characterization of
SOA constituents are hyphenated techniques that combine a
powerful chromatographic and mass spectrometric technique
such as GC/MS with prior conversion into volatile deriva-
tives and use of electron ionization (EI) or chemical ioniza-
tion (CI), and LC/MS with use of electrospray ionization
(ESI) or atmospheric pressure chemical ionization (APCI)
and detection in the negative (−) or positive (+) ion mode.
While the chromatographic separation and sensitive MS de-
tection of polar SOA constituents can, in most cases, be
readily achieved, the molecular characterization of unknown
SOA constituents remains a demanding analytical task for
various reasons. Complementary MS techniques involving
high-resolution (HR), soft ionization MS and tandem MS are
needed; detailed interpretation of MS data requires specific
knowledge and only allows one to propose chemical struc-
tures (or tentative structures), which still need to be con-
firmed through organic synthesis of the proposed compound,
or isolation of the compound and subsequent NMR analysis.

GC/MS is one of the most widely used techniques to sepa-
rate, identify and quantify individual species within aerosol
particles (Kotianova et al., 2004; and references therein). A
system with 1 h time resolution has been recently demon-
strated (Williams et al., 2006). Unfortunately, the complex-
ity of SOA can be a barrier to this type of analysis result-
ing in constantly overlapping peaks, as well as the majority
of the eluted mass being present as an “unresolved complex
mixture” (e.g., Williams et al., 2007). In addition, the ox-
idized nature of SOA makes it unsuitable for conventional
GC analysis, however the range of amenable species can be
increased using derivatization (e.g., Yu et al., 1998, 1999;
Kubátová et al., 2000; Docherty and Ziemann, 2001; Ho and
Yu, 2002; Edney et al., 2003, 2005; Claeys et al., 2004a, b,
2007; Jaoui et al., 2005; Surratt et al., 2006; Szmigielski et
al., 2007a, b; Healy et al., 2008).

Recent studies using a higher chromatographic resolution
technique, two-dimensional GC coupled to time-of-flight
(TOF) MS (GCxGC-TOFMS) have resulted in the separa-
tion of over 10 000 organic species in urban aerosol sam-
ples, some of which could be mechanistically linked to aro-
matic oxidation processes (Welthagen et al., 2003; Hamil-
ton et al., 2004). GCxGC analysis of the SOA formed in a
simulation chamber study of the photooxidation of toluene
indicated the presence of over 250 semivolatile oxidation
products demonstrating the difficulties associated with link-
ing gas phase precursors and their SOA contribution in am-
bient aerosols (Hamilton et al., 2005). This technique has re-
cently been incorporated into a field deployable thermal de-
sorption aerosol GCxGC instrument with a flame ionization

detector (2-D-TAG) which operates on a hour cycle, provid-
ing detailed OA composition at a higher time resolution than
previously possible (Goldstein et al., 2008).

LC is becoming increasingly popular for the analysis of polar compounds in aerosol and is routinely used for the analysis of carboxylic acids (Anttila et al., 2005; Römpp et al., 2006; Warnke et al., 2006). State-of-the art coupled LC/MS techniques (such as ion-trap, TOF and triple quadrupole MS) now offer similar or better detection limits than LC-fluorescence techniques and have improved greatly in terms of reliability, sensitivity and ease of use over the past 10 years. LC is particularly suited to high-MW species and very polar molecules, without the need for derivatization prior to analysis. LC/MS is finding increasing use in the analysis of the small polar fraction of SOA formed in simulation chamber studies (Larsen et al., 2001; Gao et al., 2004a, b; Surratt et al., 2006, 2007a, 2008; Iinuma et al., 2007a, b; Ng et al., 2008). LC/MS analyses have also been used to indicate that sulfate as well as nitrate groups could be incorporated in SOA as outlined in Sect. 5.1 below (Iinuma et al., 2007a, b; Surratt et al., 2007a, 2008; Gómez-González et al., 2008). The LC columns employed in most studies are of the reversed phase C18 or C8 type; however, stationary phase material containing di- or trifunctionally bonded C18 chains that prevent stationary phase collapse when aqueous eluent systems are used have proved useful in the analysis of polar sulfated SOA from the photooxidation of isoprene (Gómez-González et al., 2008; Surratt et al., 2008). Hamilton et al. (2008) recently used cationization agents to extend the range of species that could be identified and improve structural characterization. In parallel with GC separations, the low resolution afforded by a single LC separation can be overcome using comprehensive two-dimensional LC as recently demonstrated for the chemical characterization of organosulfates in β-pinene SOA (Iinuma et al., 2007b). Pol et al. (2006) also recently used LCxLC coupled to ESI-TOFMS to quantitatively identify carboxylic acids in rural and urban aerosol samples. LC has also been used to separate aerosol extracts into three fractions, based on polarity for subsequent analysis by GC/MS (Lewis et al., 1995; Shimmo et al., 2004).

Electrophoretic separations have also been used for the
analysis of SOA components. Capillary electrophoresis (CE)
can be applied to the analysis of charged species or com-
pounds that are in equilibrium with a charged form, and are
thus generally suited to carboxylic acids as summarized in
a recent review (Dabek-Zlotorzynska et al., 2008). CE can
also be coupled to MS and has been used to investigate the
organic acids and acidic oligomers in SOA formed in si-
mulation chamber studies (Iinuma et al., 2004, 2005; Müller
et al., 2007). Ion chromatography (IC) has also been used
for the separation of organic aerosol components, especially
for the analysis of very acidic, short-chain carboxylic or di-
carboxylic acids, such as oxalic acid and glyoxylic acid (Jaf-
frezo et al., 1998; Kerminen et al., 2000; Röhrl and Lammel,
2001). Mass spectrometric detection is usually not applied with IC because of the necessity to use buffers that are often not compatible with MS detection. However, there exist methods for the coupling of IC with MS detection that have been used to investigate chamber SOA and the corresponding gas-phase molecules (Fisseha et al., 2004).

One way to reduce the complexity of the analysis is to focus on groups or types of species present in the SOA, rather than trying to analyze for individual species. This simplifies the analysis of large datasets, and by analyzing the whole bulk OA mass it overcomes the disadvantage of identifying only a small fraction of the OA mass, a problem associated with GC/MS studies for example (Rogge et al., 1993). Ion exchange chromatography has been used to separate WSOC into three fractions depending on polarity, (a) neutral/basic compounds, (b) mono and di-carboxylic acids and (c) poly-carboxylic acids, followed by subsequent analysis using proton NMR (Decesari et al., 2000). Using this simplified approach, it was determined that atmospheric aerosol in the Po valley in Italy exhibits a seasonal profile, with mono- and dicarboxylic acids dominating in the summer and polycarboxylic acids in spring, winter and autumn (Decesari et al., 2001). NMR profiles of the atmospheric aerosol were found to be similar to those obtained from terrestrial fulvic acids, for example Suwannee River fulvic acid (SRFA, Cappiello et al., 2003), which has been used as a surrogate for ambient aerosol in studies of physico-chemical properties (Parsons et al., 2004; Topping et al., 2005b; Dinar et al., 2006b). This technique has been used in numerous studies, including source identification (Decesari et al., 2007) and a recent improvement involves the derivatization of the carboxylic group with diazomethane to allow a direct determination of carboxylic acids in WSOC (Tagliavini et al., 2006). Some complexities of the compound classification using this technique have been recently discussed in the literature (Collett et al., 2005; Decesari et al., 2005). However, it has the potential to separate biomass burning from marine and secondary organic aerosols as shown for a large number of monitoring stations around the world (Decesari et al., 2007).

A final type of off-line analysis is based on collection of aerosol on impactors or filters followed by analysis by FTIR spectroscopy, to determine the concentration of different organic functional groups such as saturated aliphatic (C-C-H), unsaturated aliphatic (C=C-H), aromatic (C=C-H), organosulfur (C-O-S), carbonyl (C=O), organic hydroxyl (C-OH), etc. (Blando et al., 1998; Havers et al., 1998a; Maria et al., 2003; Sax et al., 2005; Polidori et al., 2008). A particular strength of this technique may be the ability to measure the total concentrations of certain functional groups, such as amines or organosulfur species, which are difficult to quantify with other field methods. FTIR spectroscopic analysis has been applied to field samples (e.g. Maria et al., 2003; Polidori et al., 2008; Coury and Dillner, 2008, 2009; Russell et al., 2009). Maria et al. (2003) used aerosol concentrators to obtain sub 1 h time resolution with this technique

from aircraft platforms. The OA concentration determined by FTIR spectroscopy showed good agreement with AMS measurements in several studies (Gilardoni et al., 2007; Russell et al., 2009). Sequential solvent rinsing can be used to further separate the organic compounds by polarity (Maria et al., 2002, 2003; Polidori et al., 2008). The OM/OC ratio can also be estimated from these measurements (Gilardoni et al., 2009).

### 3.3.2 On-line techniques

The analytical techniques described above are based on a time integrating sampling step (using filters, impactors, etc.) followed by post-analysis. This creates the inherent risk of positive and negative artifacts due to adsorption, evaporation, and chemical reactions during the multi-step collection and analysis procedure (e.g., Turpin et al., 2000; Schauer et al., 2003; Subramanian et al., 2004; Dzepina et al., 2007). This type of sampling also severely limits both spatial and temporal sampling densities. Therefore, on-line techniques which provide real-time measurements have revolutionized the chemical analysis of aerosols. The two main types of on-line techniques currently in use are AMS and PILS-WSOC.

Since the first studies using aerosol mass spectrometers (McKeown et al., 1991), the number of on-line MS techniques has rapidly increased, and has been the subject of several reviews (Noble and Prather, 2000; Sullivan and Prather, 2005; Canagaratna et al., 2007; Murphy et al., 2007). The general principle involves the introduction of airborne particles into the instrument, followed by vaporization and ionization of the material before analysis of the ions using MS. Particle beams can be introduced in the ion source under vacuum using nozzles, capillaries or aerodynamic lenses. For the quantitative field investigation of organic aerosol components and other non-refractory material, a subset of these mass spectrometers has proven most useful.

The AMS instruments (commercialized by Aerodyne Inc.) have proven very useful for field measurements of atmospheric aerosol (Jayne et al., 2000; Jimenez et al., 2003; Canagaratna et al., 2007). The AMS combines thermal desorption (flash evaporation) of the aerosol components and EI of the desorbed components. Data are generally reported as sulfate, nitrate, ammonium, chloride and organic content (Allan et al., 2003; Jimenez et al., 2003). The EI energy of 70 eV, especially after vaporization at 600°C, results in significant fragmentation of the OA fraction and single species can hardly be identified. However, the AMS data can provide information on the bulk composition of the OA, in an analogous manner to some of the off-line techniques such as NMR and FTIR spectroscopy. Statistical multivariate techniques can be used to identify components in the total OA spectra (e.g., Zhang et al., 2005a, b; Lanz et al., 2007, 2008), as discussed below. The high time resolution (>10 Hz) of the method enables direct flux measurements with the eddy covariance technique, which is promising for biogenic SOA
quantification and aerosol deposition measurements (Nemitz et al., 2008).

Recently, the quadrupole in the AMS instrument has been replaced with a time-of-flight mass analyzer (ToF-AMS) (Drewnick et al., 2005; DeCarlo et al., 2006). The high-resolution version (up to $m/\Delta m \sim 5000$) of the ToF-AMS (HR-ToF-AMS) instrument allows the separation of ions with the same nominal mass but different elemental composition, for example at $m/z$ 43 the following fragments could be resolved: CHNO⁺, $C_2H_3O^+$, $CH_3N_2^+$, $C_2H_5N^+$ and $C_3H_7^+$. This facilitates the identification of minor elements (e.g., N, P) that were previously obscured among larger ions in the unit resolution spectra, and provides better differentiation of spectra of POA sources and SOA components, thus enhancing statistical techniques (Ulbrich et al., 2009). An ion trap has also been recently coupled with the well characterized AMS inlet and vaporization-ionization system providing the ability to perform $MS^n$ measurements (Kürten et al., 2007). A recently developed method (Aiken et al., 2007) allows the determination of O/C, N/C, and H/C atomic ratios of organic matter directly and with high time resolution. The O/C ratio of SOA produced in “traditional” high concentration chamber experiments from various precursors (α-pinene, aromatics, isoprene) is in the range 0.28–0.43 (Aiken et al., 2008). These ratios are lower than those found in the atmosphere and are attributed to less aging and the high precursor concentrations used in the experiment which allows less oxidized species to partition into the aerosol (Aiken et al., 2008; Shilling et al., 2009; Huffman et al., 2009). For urban aerosol the O/C atomic ratio increases with aging of the aerosol and reaches 1 for the aged SOA fraction (OOA1). A direct correlation between the measured O/C and OM/OC ratios was found yielding the relationship

$$OM/OC = (1.26 \times O/C) + 1.18 \quad (4)$$

Mixed urban aerosols have OM/OC ratios of 1.6–1.8, which is on the higher side of the range of 1.6±0.2 reported by Turpin and Lim (2001). Aged regional organic aerosols have OM/OC ratios of ca. 2.1 which is in good agreement with the value of 2.1±0.2 recommended for non-urban aerosols (Turpin and Lim, 2001). In the water soluble fraction of organic aerosol OM/OC ratios of 1.8–1.93 were obtained for ambient SOA using $^1H-NMR$ (Decesari et al., 2007).

Laser-ablation mass spectrometers can analyze individual particles by using a laser to vaporize and ionize single particles followed by TOFMS (McKeown et al., 1991; Noble and Prather, 1996; Murphy, 2007) and allow the analysis of positive and negative ions simultaneously (Hinz et al., 1996). An instrument of this type, the aerosol TOF mass spectrometer (ATOFMS), is commercially available from TSI Inc. The OA fraction produces characteristic fragmentation patterns, but matrix effects make quantification difficult. Murphy et al. (2006) calibrated the organic/sulfate ratios of their Particle Analysis by Laser Mass Spectrometry (PALMS) instrument by comparison with the AMS instrument, and showed that

30% to over 80% of the aerosol mass in the free troposphere is carbonaceous material over the regions they have studied, which may be related to SOA production but also to biomass burning or reduced cloud scavenging of organic-dominated particles. Under certain conditions (e.g., matrix composition, instrument tuning), fragmentation in laser-ablation instruments can be reduced and higher-MW compounds, such as oligomers formed in chamber experiments, can be identified with this technique (Gross et al., 2006; Denkenberger et al., 2007).

Over the last decade there have been numerous other online mass spectrometers incorporating a range of desorption and ionization methods. Some of these designs have been used in field studies, while others have been limited to laboratory applications, primarily due to sensitivity limitations. Of note among the instruments that have only been used for laboratorial applications is the photoelectron resonance capture ionization AMS (PERCI-AMS) instrument, where particles are deposited onto a resistively heated wire and the vaporized material is ionized with low energy electrons (LaFranchi et al., 2004; Zahardis et al., 2006). Some important laboratory findings about SOA and heterogeneous chemistry have arisen from work with the thermal desorption particle beam mass spectrometer (TDPBMS), which captures particles in a cryo-cooled surface and then desorbs them by slow heating allowing volatility separation before MS analysis (Docherty and Ziemann, 2003; Lim and Ziemann, 2005). A similar volatility separation technique, involving a rapidly switching thermodenuder, has recently been developed for field sampling with the AMS instrument (Huffman et al., 2008, 2009). Warscheid and Hoffmann (2001, 2002) have used APCI, where particles are vaporized in a heated tube and organic compounds chemically ionized using a corona discharge, producing mostly molecular ion species. This instrumentation allows structural analysis of single species using an ion trap MS (ITMS). Other soft ionization techniques based on photoionization (Northway et al., 2007; Dreyfus and Johnston, 2008) or CI (Hearn and Smith, 2006) have been developed and are starting to be applied to ambient measurements. The analysis of the elemental composition of sub-20 nm particles has recently become possible with the nano aerosol mass spectrometer (NAMS) (Wang et al., 2006), while their molecular composition can be analyzed by thermal desorption chemical ionization mass spectrometry (TDCIMS) (Smith et al., 2005). Recently the commercially available PTR/MS (proton transfer reaction mass spectrometry) instrument, which detects organic compounds in the gas-phase using $H_3O^+$ as the chemical ionization reagent, was successfully applied to the identification of organic species in the particle phase (Hellén et al., 2008b).

Particle into liquid samplers (PILS) collect particles into water for subsequent analysis (e.g., IC) (Weber et al., 2001; Orsini et al., 2003; Sullivan et al., 2004; Sorooshian et al., 2006a). A continuous measurement of WSOC (as well as inorganic and organic ions by IC) with a time resolution of
minutes has been coupled to a PILS instrument and deployed in several aircraft campaigns (e.g., Sorooshian et al., 2006a, b, 2007a, b; Peltier et al., 2007a; Weber et al., 2007). The main sources of WSOC are SOA and biomass burning OA (Sullivan et al., 2006). Miyazaki et al. (2006) showed that WSOC in Tokyo correlated well with SOC estimated with the EC-tracer method, while water-insoluble organic compounds (WIOC) correlated well with EC and CO in that study. Kondo et al. (2007) and Docherty et al. (2008) have shown that the WSOC measurement is very similar to the total AMS OOA in Tokyo and Riverside, respectively. Based on measurements made in Cairo, Favez et al. (2008) suggested that anthropogenically-dominated SOA may have a higher insoluble fraction. Recently, a measurement of WSOC following PILS collection has also been demonstrated in the field, and was found to be in very good agreement with semi-continuous OC measurements (a slope close to unity was obtained within the uncertainties of both measurements). However, a non-zero intercept was also determined, which is possibly related to the inability of the total organic carbon (TOC) analyzer to digest larger insoluble particles (Peltier et al., 2007b). Another very promising recent development is the simultaneous measurement of particle and gas-phase WSOC by Hennigan et al. (2008, 2009). During the summer in Atlanta at elevated RH levels (>70%), a significant increase in WSOC partitioning to the particle phase was observed and followed the predicted water uptake by fine particles. These results suggest that SOA formation involving partitioning to liquid water may be a significant pathway that is often not considered. Sorooshian et al. (2007a, b) have pioneered the analysis of organic acids by IC analysis following PILS collection. These authors report that organic acids averaged 3.4% of the water-soluble PM mass during an airborne study in the Houston area. Organic acids were most abundant above clouds, presumably as a result of aqueous phase chemistry in cloud droplets, followed by subsequent droplet evaporation above cloud tops with the main product of this chemistry being oxalic acid. Suppressed organic acid formation was observed in clouds with relatively acidic droplets and lower liquid water content.

### 3.3.3 Analysis of high molecular weight compounds

During the last decade an increasing number of studies have reported that macromolecular species make a significant contribution to the mass of organic compounds present in atmospheric aerosol. The largest fraction of these species showed considerable similarities in structural properties to humic and fulvic acids and were thus termed humic-like substances (HULIS) (Havers et al., 1998a, b; Zappoli et al., 1999; Decesari et al., 2000; Gelencsér et al., 2000a, b; Kiss et al., 2002). HULIS can affect many aerosol properties, including hygroscopicity, cloud condensation nuclei (CCN) activity, surface tension and optical parameters (Gysel et al., 2004; Kiss et al., 2005; Dinar et al., 2006b, 2007, 2008a,

b; Hoffer et al., 2006; Salma et al., 2006; Taraniuk et al., 2007; Wex et al., 2007). HULIS are operationally defined fractions of the aerosol and their quantification thus depends to some extent on the applied extraction, isolation and detection method. This fraction consists of polyacidic compounds of aliphatic and aromatic structures with additional substituted functional groups. Their molecular mass was determined to be between 150–500 Da (Kiss et al., 2003). In a comprehensive review, Graber and Rudich (2006) concluded that, although it is difficult to distinguish atmospheric HULIS from terrestrial and aquatic humic substances on a chemical level, there are significant differences in physical properties such as hygroscopicity and CCN activity. This has been substantiated in a series of studies on the microphysical properties of HULIS extracted from real aerosol samples (Dinar et al., 2006a, b, 2007; Asa-Awuku and Nenes, 2007; Taraniuk et al., 2007; Wex et al., 2007). There is indication that HULIS can be of primary origin, e.g., wood combustion, but may also be associated with secondary particle-phase production (Gelencsér et al., 2002; Samburova et al., 2005a, b; Feczko et al., 2007; Surratt et al., 2007a, 2008). LC separations are well suited to the analysis of HULIS in atmospheric aerosols and a comprehensive review of extraction and analysis techniques is given in Graber and Rudich (2006). Separation using reverse-phase high-performance LC (HPLC) and size-exclusion chromatography (SEC) has indicated that aerosol HULIS is of lower molecular mass than terrestrial and aquatic humic substances, possibly due to the presence of mineral acids and mono- and dicarboxylic acids which prevent the formation of large supramolecular structures. Samburova et al. (2005a, b) used SEC coupled to UV spectroscopy and laser desorption ionization MS (LDI-MS) to determine an upper value of 700 Da for the molecular mass of HULIS in atmospheric aerosol and showed that this corresponds to around 9–30% of the total organic carbon for an urban background site.

Higher-MW reaction products (i.e., products with MWs higher than those of first- and higher- generation oxidation products) have been identified in laboratory SOA produced from the atmospheric oxidation of a wide range of compounds, including 1,3,5-trimethylbenzene, cycloalkenes, α-pinene and isoprene (Gao et al., 2004a, b; Iinuma et al., 2004, 2007a; Kalberer et al., 2004, 2005; Tolocka et al., 2004; Bahreini et al., 2005; Hastings et al., 2005; Domin et al., 2006; Surratt et al., 2006; Szmigielski et al., 2007a; Hamilton et al., 2006), as well as the hydration of glyoxal (Hastings et al., 2005). These higher-MW reaction products have been denoted as “oligomers”; however, it is worth noting that higher-MW products identified in SOA and ambient aerosol do not only contain oligomers, which according to the IUPAC recommendations denotes products which comprise a plurality of monomeric units derived from molecules of lower molecular mass (IUPAC, 1996). In addition to oligomers, higher-MW products also comprise other classes of compounds such as organosulfates and nitrooxy
organosulfates (Romero and Oehme, 2005; Reemtsma et al., 2006; Iinuma et al., 2007a, b; Gómez-González et al., 2008; Surratt et al., 2007a, 2008), and dimers such as hemiacetals formed between 2-methyltetrols and a C5-dihydroxycarbonyl (Surratt et al., 2006). A word of caution is also appropriate here with regard to the use of the term “humic-like substances” (HULIS), which may comprise a complex mixture of several classes of polyfunctional compounds, including oligomers, organosulfates and nitrooxy organosulfates. In earlier work (MacCarthy et al., 1990) it has been stated that the term “structure of humic substances” must not be interpreted in the conventional chemical context because such fine detail was simply beyond reach at that time. Owing to the considerable developments of MS techniques based on ESI, tandem MS and high-resolution MS over the last two decades, the analytical tools are now available to efficiently address the polyfunctional chemical structures of polar HULIS in complex mixtures such as SOA and ambient aerosol.

Initial evidence for oligomer formation was provided by off-line LDI-MS (Kalberer et al., 2004), but this approach did not provide detailed structural information. Tolocka et al. (2004) applied matrix-assisted laser desorption-ionization (MALDI), ESI and CIMS. CI was found to cause decomposition of the parent oligomer, but MALDI and ESI provided similar oligomer distributions. Tandem MS using a quadrupole TOF (Q-TOF) indicated that the dimers, trimers and tetramers were composed of known gas phase reaction products from the ozonolysis of α-pinene. Gao et al. (2004a) used LC coupled to a quadrupole mass spectrometer (LC/QMS) and direct infusion into an ion trap mass spectrometer (ITMS), both of which were equipped with ESI, to detect oligomers with masses up to 1600 Da in SOA formed during cyclohexene ozonolysis and estimated that they accounted for around 10% of the total mass fraction. Fragmentation patterns for structural analysis were performed in negative ionization mode but a wider range of species could be detected in positive ionization mode as $[M+Na]^+$ adducts.

On-line MS techniques have also been used to follow the temporal evolution of oligomers in simulation chamber studies. Gross et al. (2006) used an aerosol time of flight mass spectrometer (ATOFMS) with a minute time resolution to follow the increasing MW of oligomers formed during the photooxidation of α-pinene and 1,3,5-trimethylbenzene. Single particle ATOFMS has also been used for the identification and real-time monitoring of oligomers in aged ambient atmospheric aerosol in California, USA (Denkenberger et al., 2007). Müller et al. (2008) have applied on-line APCI tandem MS (APCI-MS^n) to investigate oligomer formation in real-time and used MS^n for structural characterization.

Fourier transform ion cyclotron resonance MS (FTICRMS) has ultra-high mass accuracy (sub ppm) and a very high mass resolution (>100 000) allowing determination of molecular composition even at higher mass-to-charge

ratios. Reinhardt et al. (2007) used FTICRMS to investigate the molecular composition of oligomeric species in α-pinene SOA and applied Kendrick mass analysis, a tool employed in petroleomics, where nominal mass is plotted against the Kendrick mass defect to visualise the highly complex dataset as homologous series of oligomers with increasing numbers of CH₂ groups and O. It was found that the monomer units had a higher O/C ratio than the dimers and trimers, indicating that condensation reactions involving the loss of water are important in the formation of oligomers. Reemtsma et al. (2006) have also applied FTICRMS to the analysis of ambient urban aerosol and reported that the high-MW species observed resemble fulvic acids and are often sulfated and/or nitrated. High-resolution mass analyzers in combination with (–)ESI have recently been employed to support the elemental composition of organosulfates and nitrooxy organosulfates present in BSOA and ambient aerosol. TOF mass analyzers of the reflector type with a mass resolution >10 000 were used in most of these studies (Romero and Oehme, 2005; Iinuma et al., 2007a, b; Surratt et al., 2008) but another mass analyzer with ultra-high mass resolution (>100 000) in addition to the ion cyclotron, i.e., the Orbitrap (Gómez-González et al., 2008) has also been employed in the analysis of organosulfates and nitrooxy organosulfates.

# 4 Recent developments in laboratory, field and modeling studies

## 4.1 Laboratory studies

The large majority of our knowledge about SOA formation from VOC oxidation derives from laboratory studies. Most of these studies make use of large simulation chambers (smog chambers), ranging in volume from 1–270 m³ (see Table 2), although some complementary studies have been conducted in aerosol flow reactors (e.g., Bonn et al., 2002; Berndt et al., 2003; Jonsson et al., 2006, 2008a, b). In general, chamber experiments are carried out in batch mode, though some use a continuous flow mode. They are designed to mimic atmospheric conditions as closely as possible so that nonlinearities and other complicating factors are shared in the experiment and in the real atmosphere. Flow reactor experiments, on the other hand, are more often designed to isolate specific fundamental terms in a chemical mechanism, and are primarily not used to explicitly mimic the ambient atmosphere.

At their heart, almost all SOA experiments are based on a mass balance, typically expressed as in Eq. (3). As noted above though, there are many complicating factors in the interpretation of experimental yields, several of which have yet to be completely resolved. With these factors in mind, the objective of laboratory SOA formation experiments can be refined to a dual purpose. First is to constrain the yields ($\alpha_i$)
<table><caption>**Table 2.** Selected laboratory chamber facilities for the study of secondary organic aerosols.</caption><thead><tr><th>Location</th><th>Type</th><th>Volume (m³)</th><th>Material</th><th>Temperature (K)</th><th>Reference<sup>a</sup></th></tr></thead><tbody><tr><td>California Institute of Technology, USA</td><td>Indoor Photoreactor</td><td>28 (dual)</td><td>FEP</td><td>290–303</td><td>Cocker et al. (2001b)<br>Ng et al. (2008)</td></tr><tr><td>Carnegie Mellon University, USA</td><td>Indoor Photoreactor</td><td>10</td><td>PTFE/FEP</td><td>288–313</td><td>Stanier et al. (2007)<br>Robinson et al. (2007)</td></tr><tr><td>Forschungszentrum Jülich, Germany (SAPHIR)</td><td>Outdoor Photoreactor</td><td>270</td><td>FEP</td><td>ambient</td><td>Rohrer et al. (2005)</td></tr><tr><td>Forschungszentrum Jülich, Germany</td><td>Dark Chamber</td><td>250</td><td>PTFE/FEP</td><td>ambient</td><td>Mentel et al. (1996)<br>Folkers et al. (2003)</td></tr><tr><td>Forschungszentrum Karlsruhe (AIDA)</td><td>Dark Chamber</td><td>4–84</td><td>Metal</td><td>183–323</td><td>Saathoff et al. (2003)<br>Jonsson et al. (2007)</td></tr><tr><td>Fundación Centro de Estudios Ambientales del Mediterráneo, Spain (EUPHORE)</td><td>Outdoor Photoreactor</td><td>200</td><td>FEP</td><td>ambient</td><td>Klotz et al. (1998)<br>Martin-Reviejo and Wirtz (2005)</td></tr><tr><td>Leibniz Institute for Tropospheric Research, Germany</td><td>Indoor Photoreactor</td><td>19</td><td>FEP</td><td>289–308</td><td>Iinuma et al. (2007b)</td></tr><tr><td>Paul Scherrer Institute, Switzerland</td><td>Indoor Photoreactor</td><td>27</td><td>FEP</td><td>288–313</td><td>Paulsen et al. (2005)<br>Kalberer et al. (2004)</td></tr><tr><td>University College Cork, Ireland</td><td>Indoor Photoreactor</td><td>6.5</td><td>FEP</td><td>293–305</td><td>Temime et al. (2007)<br>Healy et al. (2008)</td></tr><tr><td>University of Manchester, UK</td><td>Indoor Photoreactor</td><td>18</td><td>FEP</td><td>288–313</td><td></td></tr><tr><td>Univ. of California, Riverside, USA (CE-CERT)</td><td>Indoor Photoreactor</td><td>90 (dual)</td><td>FEP</td><td>278–323</td><td>Carter et al. (2005)<br>Song et al. (2005)</td></tr><tr><td>Univ. of California, Riverside, USA (APRC)</td><td>Indoor Photoreactor</td><td>6–8 (several)</td><td>PTFE/FEP</td><td>ambient</td><td>Tobias and Ziemann (1999)<br>Matsunaga et al. (2009)</td></tr><tr><td>Univ. of North Carolina, USA</td><td>Outdoor Photoreactor</td><td>120, 137 (dual), 150 (dual)</td><td>FEP</td><td>ambient</td><td>Lee et al. (2004)<br>Li et al. (2007)</td></tr><tr><td>US Environmental Protection Agency</td><td>Indoor Photoreactor</td><td>14.5</td><td>FEP/TFE</td><td>293–298</td><td>Edney et al. (2005)<br>Offenberg et al. (2007)</td></tr><tr><td>CSIRO Energy Technology Australia</td><td>Indoor Photoreactor</td><td>18</td><td>FEP</td><td>ambient</td><td>Hynes et al. (2005)<br>Angove et al. (2006)</td></tr><tr><td>National Institute for Environmental Studies, Japan</td><td>Indoor Photoreactor</td><td>6</td><td>PTFE/FEP</td><td>ambient</td><td>Sato et al. (2007)</td></tr></tbody></table>

of a set of (semi- or non-) volatile products ($P_i$) as a function of the appropriate variables (temperature, relative humidity, NOₓ and hydrocarbon mixing ratios, UV intensity, etc.), recognizing that $P_i$ are usually surrogate or lumped species that “average” over the real properties of multiple real product species. Second is to elucidate fundamental aspects such as chemical mechanisms and phase partitioning of a given mixture of products in an experiment and then extend that knowledge to the atmosphere. Here several variables potentially influence both the chemical mechanisms and the phase-partitioning thermodynamics. The most notable is temperature, followed by water vapor. A description of the influence

of water vapor is found in Sect. 5.6. The effect of temperature on SOA formation has received comparatively little attention (Takekawa et al., 2003; Jonsson et al., 2007, 2008b; Pathak et al., 2007, 2008; Saathoff et al., 2009). However, the results obtained to date provide evidence for a temperature effect, both on gas-particle partitioning and on the chemical mechanism.

### 4.1.1 Aerosol yield

To obtain constraints on $C_i^*$ and $\alpha_i$, data are required spanning a range of $C_{OA}$. Historically, multiple chamber experiments (each lasting one or more days) were carried out to

<footnote>a Selected references with emphasis on SOA (maximum 2 per laboratory).</footnote>
obtain $F_{OA}$ versus “final” $C_{OA}$ (Odum et al., 1996; Hoffmann et al., 1997; Griffin et al., 1999), though more recently individual chamber experiments have been used to obtain “dynamic” $C_{OA}$, using the increase in $C_{OA}$ as $C_{HC}$ decreases due to oxidation (see e.g., Hurley et al., 2001; Ng et al., 2006; Presto et al., 2006). The simple fitting of $F_{OA}$ versus $C_{OA}$ data in this manner is valid only if the set of reaction products and yields remains the same, either through a succession of experiments or during an individual experiment.

At least three approaches have been applied to fitting or reproducing SOA data based on this foundation: (1) Explicit enumeration of reaction products in a reaction mechanism including partitioning (Jenkin, 2004), (2) empirical determination of both $\alpha_i$ and $C_i^*$ in a n-product model, most often with n=2 (Odum et al., 1996), and (3) fitting of $\alpha_i$ only for a fixed “basis set” of $C_i^*$ (Presto and Donahue, 2006). In all cases, the objective is to constrain the amount and volatility of the reaction products, especially those that partition to the particulate phase. The methods are all empirical, so they cannot constrain volatility outside of the range of $C_{OA}$ in the data; consequently, a recent focus has been to develop SOA formation data over atmospherically relevant $C_{OA}$ ranges (0.1–100 $\mu g m^{-3}$, Presto and Donahue, 2006; Shilling et al., 2009). There are two separate issues: first is the ability of fit parameters to reproduce data ($F_{OA}$), and second is the interpretation of those parameters. As an example, Fig. 5 shows data from $\alpha$-pinene + ozone experiments using 2-butanol as an OH radical scavenger (Griffin et al., 1999; Cocker et al., 2001a; Presto and Donahue, 2006; Pathak et al., 2007). The log x axis allows the wide atmospheric range (0.1–100 $\mu g m^{-3}$) to be observed. The function shown is a basis-set fit; as the data are in quite good agreement over this range, the fit interpolates well. Regardless of the fitting approach, the data themselves reveal that a significant fraction of the overall product mass has a volatility ($C^*$) in the 5–500 $\mu g m^{-3}$ range, because the observed values of $F_{OA}$ rise sharply over this range. Likewise, the mass yield of products less volatile than 1 $\mu g m^{-3}$ can be no more than about 0.05. However, these conclusions about product volatility distributions assume that the composition of the VOC oxidation products does not vary as $C_{OA}$ changes by several orders of magnitude. This may be a reasonable assumption for ozone + alkene reactions such as ozone + $\alpha$-pinene, but recent results suggest that this may not be the case for more complex photooxidation systems (Chan et al., 2007; Duplissy et al., 2008). It should also be noted that recent experiments point out the challenge in establishing the yield at very low values of $C_{OA}$ (Shilling et al., 2009).

Obviously this parameterization needs to be performed for each selected condition. Here the influence of $NO_x$ on gas-phase oxidation mechanisms for SOA formation has received increasing attention (Kroll et al., 2005b, 2006; Presto et al., 2005b; Ng et al., 2007a, b; Zhang et al., 2006). Explicit mechanisms can of course directly handle such dependencies, but for lumped mechanisms Presto and Donahue (2006)

<figure><img src="image_7.png" /><figcaption>Fig. 5. Aerosol mass fraction ($F_{OA}$) for SOA generated in the reaction of $\alpha$-pinene and ozone under low-$NO_x$ conditions using 2-butanol as an OH scavenger. Data points are taken from; Griffin et al. (1999) (triangles); Cocker et al. (2001a) (squares); Presto and Donahue (2006) (circles); Pathak et al. (2007) (stars).</figcaption></figure>

proposed a simple parameterization of this dependence based on the $RO_2 + NO$ branching ratio, $\beta$. Low- and high-$NO_x$ product yields ($\alpha$) are based on photochemical chamber studies carried out under extremes of $NO_x$ concentration. The overall product yields are then parameterized as the linear combination,

$$ (\alpha) = \beta (\alpha)^{\text{high-NO}_x} + (1 - \beta)(\alpha)^{\text{low-NO}_x} \quad (5) $$

Ng et al. (2007a) and Henze et al. (2008) applied a similar formulation for the generation of SOA from aromatics. However, the effect of $NO_x$ on SOA mass yield is complex. For most light precursors, including alkylbenzenes, isoprene, and monoterpenes, SOA formation at high $NO_x$ appears to be reduced (Johnson et al., 2004, 2005; Kroll et al., 2005b, 2006; Presto et al., 2005b). In the ozonolysis of limonene, which contains two double bonds, SOA formation is broadly similar at all $NO_x$ levels (Zhang et al., 2006). Finally, for photooxidation of sesquiterpenes, SOA formation is enhanced at high $NO_x$ (Ng et al., 2007b). It should be noted that adding $NO_x$ during dark reactions will enhance oxidation of the reactant by the $NO_3$ radical (Hallquist et al., 1999; Spittler et al., 2006). Obviously, the $NO_3$ radical initiated oxidation of VOCs can be a source of SOA during nighttime conditions, as outlined in Sect. 3.1. The reaction of VOCs with Cl atoms is also a potential source of SOA in the marine boundary layer (e.g., Cai et al., 2008). For further discussion of the chemical mechanisms of SOA formation, see Sect. 3.1 and the recent reviews by Kroll and Seinfeld (2008) and Johnson and Marston (2008).
### 4.1.2 Chemical time scales versus precursor and SOA levels

An important challenge is associated with scales: chemical timescales compared with experimental timescales, mass and concentration scales in experiments versus the ambient atmosphere, and the physical scales of the experiments themselves. In most experiments, this means that much higher concentrations of a precursor, $C_{HC}$, must be oxidized than one ever finds in the atmosphere in order to generate enough SOA: experiments at the typical $C_{HC}$ of an individual hydrocarbon alone would generate trivial levels of $C_{OA}$ that would be very hard to quantify experimentally, and thus are not experimentally appropriate. Consequently this may also influence the choice of oxidant level where experimental interpretation is more straightforward when the product formation timescale is much shorter than the particle deposition timescale. This sometimes motivates experimentalists to utilize oxidant levels well over typical ambient levels; however, great care must be taken to ensure that the resulting chemical mechanism and products remain relevant to the atmosphere. At a low organic aerosol level, only the least volatile VOC oxidation products effectively partition into the aerosol phase. As the mass concentration of organic aerosol rises in a chamber experiment, compounds of higher volatility progressively partition into the aerosol phase. To best simulate partitioning under ambient conditions, organic aerosol levels in chamber SOA studies should be within the range typically encountered in the atmosphere, roughly from 0.1 to $100 \ \mu g \ m^{-3}$. However, higher mass experiments can be of considerable value too as they help constrain the full volatility distribution of the oxidation products for modeling of extended atmospheric transport and aging. The production of high mass yields is also advantageous when trying to identify individual constituents of SOA that may be used as markers of particular sources of SOA in ambient aerosol, as described in Sects. 4.2 and 5.1.

The organic aerosol level $C_{OA}$ ($\mu g \ m^{-3}$) affects both the degree of partitioning of the array of semivolatile products as well as the kinetics of SOA formation (Chan et al., 2007; Kroll et al., 2007; Kroll and Seinfeld, 2008). Also, when $C_{OA}$ is small, there is an induction period associated with subsaturation of the semivolatile product relative to the amount required for condensation. During this induction period, a substantial fraction of condensable material may be lost to the walls, resulting in lower yields (Kroll et al., 2007). In interpreting yields from such a series of experiments, it is important to note that the SOA generated at a certain value of $\Delta M_{HC}$ is not necessarily the same for different starting hydrocarbon concentrations (Chan et al., 2007). In short, to produce the same value for $\Delta M_{HC}$, a longer time is required for a smaller value of initial $C_{HC}$. When generation of semivolatile products involves multiple oxidation steps, as it often does, during that longer time more SOA can form. The result is that at a given $\Delta M_{HC}$, the measured SOA yield

can be larger for smaller initial hydrocarbon concentrations. Care must be exercised in interpreting SOA production data from experiments carried out at different initial concentrations when the parent VOC is not completely reacted, or indeed whenever multiple generations of oxidation occur in an experiment. The effect of the initial organic aerosol level on the kinetics of SOA formation is accentuated even more if the semivolatile oxidation products undergo gas-phase decomposition reactions to give more volatile species (Kroll et al., 2007).

### 4.1.3 Recent changes in yield estimates

Recent simulation chamber experiments have shown rather dramatic changes in the yield parameters for important SOA precursor compounds, as illustrated in Fig. 6. Newer data typically show much more aerosol formation than found in papers published prior to 2005 (Ng et al., 2006, 2007a, b; Pathak et al., 2007). For example, Ng et al. (2007a) showed that under low-$NO_x$ conditions the yield of SOA from aromatic compounds could be more than 30%, regardless of $C_{OA}$. Under high-$NO_x$ conditions the same aromatic compounds had much reduced yields, but still significantly higher than found in the earlier studies of Odum et al. (1996). These large increases in yield estimates have been attributed by Ng et al. (2007a) to improvements in the methodology used in the chambers: faster-reacting systems to avoid wall-loss of formed SOA, experiments that were conducted at either high or low $NO_x/HO_2$ ratios throughout, and a greater recognition of the kinetic factors controlling experimental yield values (Chan et al., 2007). An important new development has been the recognition that isoprene, a compound previously thought to be unimportant for SOA formation under atmospherically relevant conditions (Pandis et al., 1991), can also produce SOA and likely in very significant quantities (Claeys et al., 2004a, b; Edney et al., 2005; Kroll et al., 2005b, 2006; Dommen et al., 2006; Henze and Seinfeld, 2006; Surratt et al., 2006; Tsigaridis and Kanakidou, 2007).

### 4.1.4 Density and SOA morphology

An estimate of particle density (and an assumption of sphericity) is needed when volume measurements of SOA are converted into mass concentration, such as in SOA yield calculations using differential mobility analyzer (DMA) measurements of the size distribution. Without all of the significant components of the SOA being identified, the estimation of the density based on the chemical composition is highly uncertain. A number of methods to determine particle density have been reported in the literature (McMurry et al., 2002; DeCarlo et al., 2004). Typically for SOA an effective density is derived by combining measurements of aerodynamic and mobility diameters (McMurry et al., 2002; DeCarlo et al., 2004; Zelenyuk et al., 2006; Kostenidou et al., 2007), or aerodynamic and optical diameters (Murphy et al., 2004; Cross et al., 2007). The effective densities thus determined are equivalent to the material density in the case
of spherical particles but may differ significantly from the material density and other definitions of effective density for particles with irregular shapes (DeCarlo et al., 2004).

Laboratory measurements of SOA formed from anthro-
pogenic and biogenic precursors yield aerosol effective den-
sities in the range 1.06–1.45 g cm⁻³ and 0.64–1.65 g cm⁻³,
respectively (Bahreini et al., 2005; Alfarra et al., 2006;
Kostenidou et al., 2007). A density of 1.4 g cm⁻³ has been
determined for isoprene (Dommen et al., 2006; Ng et al.,
2008) and aromatic (Alfarra et al., 2006; Ng et al., 2007a)
SOA by comparing mobility diameter measurements from a
differential mobility analyzer with the vacuum aerodynamic
diameter measured with an on-line aerosol mass spectrom-
eter. In the absence of direct measurements, it is recom-
mended that a value of 1.4 g cm⁻³ is used for the SOA den-
sity, particularly when determining SOA mass yields from
measured volume concentrations, as performed in several re-
cent studies (e.g., Ng et al., 2007a, b; Healy et al., 2008).
The influence of experimental conditions and other parame-
ters on SOA density is largely unknown. The effective den-
sity of SOA from cycloalkene ozonolysis was found to be
higher in the absence of seed aerosol than in the presence
of (NH₄)₂SO₄ seed aerosol (Bahreini et al., 2005). Alfarra
et al. (2006) showed that, for non-seeded experiments with
1,3,5-trimethylbenzene and α-pinene, SOA densities are in-
dependent of the particle size for mobility diameters in the
range 140 to 300 nm. Shilling et al. (2009) reported that the
effective organic density was as high as 1.73±0.13 g cm⁻³
for a loading of 0.46 µg m⁻³, but decreased with increas-
ing aerosol load, in line with decreasing degree of oxidation.
The effects of parameters such as temperature and relative
humidity on SOA density remain little characterized to date.
It is recommended that future studies report the exact experi-
mental conditions (precursor and oxidant concentration, seed
concentration and composition, relative humidity, tempera-
ture) to enable reliable evaluations and comparisons to be
made.

Changes in the density of organic aerosol due to chem-
ical reaction with ozone and OH have been investigated in
a number of recent laboratory studies. Katrib et al. (2005)
studied the effect of ozone on the density of an oleic acid
outer layer on polysterene latex cores and found that the layer
density increased with increasing ozone exposure, consistent
with the layer becoming more oxidized. Similarly George et
al. (2007) showed that the density of bis(2-ethylhexyl) seba-
cate particles exposed to OH radicals increases with increas-
ing exposure to the oxidant. It has also been shown that the
density of HULIS extracted from aged atmospheric aerosol is
higher than the density of HULIS present in fresh aerosols.
The aged aerosol also exhibited a higher value for the O/C
ratio (Dinar et al., 2006a). It is thus likely that the density
of atmospheric organic aerosol will increase during its at-
mospheric residence time. Measurements of organic aerosol
density in the atmosphere could thus provide information on
atmospheric processing of particles.

<figure><img src="image_8.png" /><figcaption>**Fig. 6.** Comparison of “old” and “new” SOA partitioning fractions ($F_i$ values, see Eq. 2) for an assumed background aerosol concentration of $C_{OA}=5~\mu g~m^{-3}$. Data from Odum’96, Griffin’99 have been adjusted to a temperature of 295 K (from an assumed 305 K, experiments were 303–310 K), density $1.48~g~cm^{-3}$ for m-xylene, following Ng et al. (2007), $1.25~g~cm^{-3}$ for α-pinene. (Read e.g., Kroll’06 as Kroll et al. (2006). Cited papers are those giving yield-curve parameters. Consult these for original experimental data.)</figcaption></figure>

Field data on effective densities of the organic aerosol
component are not routinely reported. Cross et al. (2007) de-
termined the organic aerosol effective density by combining
measurements of light scattering and aerosol vacuum aero-
dynamic diameters. The effective density of organic aerosol
was found to be 1.27 g cm⁻³ at Chebogue Point during the
ICARTT study in summer 2004, when OA was highly oxy-
genated and thus likely dominated by SOA (Zhang et al.,
2007a; de Gouw et al., 2008). Clearly, more detailed studies
of the effective density of ambient organic aerosol and how
it changes with aging and processing in the atmosphere are
required.

The physical and chemical morphology of SOA has not
been extensively investigated to date. Model results reported
by Marcolli et al. (2004) suggest that tropospheric particles
in a highly mixed state are generally not solid. Thus the pres-
ence of SOA in atmospheric particles should preferentially
lead to particles that are liquid or waxy. However ambient
organic-dominated particles with high SOA content in Mexi-
co City have been shown to be non-spherical (Salcedo et al.,
2007) and to bounce as much as sulfate from the AMS va-
porizer (Salcedo et al., 2006) which suggests that they may
be solid. SOA has been suggested to form coatings on aque-
ous inorganic aerosol and thus influence the reactivity of the
aerosol surface (Anttila et al., 2007). On short time scales
particle hygroscopicity (Chan et al., 2006) and CCN activity
could also possibly be affected as described in Sect. 5.6. In
addition, the viscosity of organic particles can be enhanced
by oxidation and oligomerization which may also inhibit
liquid-phase reactions.
## 4.2 Field studies

Measurements of SOA in the field are subject to the balance between completeness and chemical resolution, as described in Sect. 3.3. In many cases, the detection of SOA in ambient atmospheric aerosol relies on measurement of suitable marker compounds identified from laboratory experiments. A list of marker compounds, indicative of different sources of SOA is given in Table 3. The identification of BSOA marker compounds is further elaborated in Sect. 5.1. Here, the strategies and methods employed for quantification of SOA in selected recent field studies will be surveyed.

### 4.2.1 Indirect SOA quantification methods

Until recently there were no methods that could directly quantify the SOA mass separately from the POA mass. Two indirect SOA estimation techniques based on tracers have been used for more than a decade. The elemental carbon (EC) tracer method (Turpin and Huntzicker, 1995) is based on measurements of EC and organic carbon (OC), which can now be performed with a time resolution of 1 h. If a (OC/EC)\_pri ratio can be determined, the primary OC can be estimated from the EC measurement and the OC\_sec can be calculated as OC-OC\_pri. (e.g., Castro et al., 1999; Cabada et al., 2004). A variant of this method using the ratio of organic aerosol to carbon monoxide has been proposed recently, which takes advantage of the higher time resolution of carbon monoxide measurements (Takegawa et al., 2006; Docherty et al., 2008). The main weakness of these methods is the difficulty of estimating a representative value for (OC/EC)\_pri or (OA/CO)\_pri. The common method of assuming that ambient SOA is negligible during the morning rush hours has been shown to lead to a significant overestimation of these primary ratios and underestimation of SOA in several studies (Zhang et al., 2005b; Docherty et al., 2008). The primary ratio also depends on the fraction of diesel versus gasoline traffic which can change with time of the day and day of the week (Harley et al., 2005), and the ambient organic mass concentrations according to partitioning theory (Robinson et al., 2007). Furthermore, in areas where both wood (or other biomass) burning and traffic emissions are significant, it becomes almost impossible to estimate SOA using the OC/EC method because of the very different OC/EC ratios of these particle sources (Szidat et al., 2006).

A more sophisticated approach for POA estimation uses the chemical mass balance (CMB) of molecular tracers (Schauer et al., 1996; Shrivastava et al., 2007; Ke et al., 2008). It has been pointed out that condensed-phase organic tracers may be lost by heterogeneous oxidation (Robinson et al., 2006), potentially resulting in a low bias for these methods. The CMB approach tended to identify a high fraction of SOA only during “air pollution episodes” although a recent study performed in the Los Angeles Basin (Docherty et al., 2008) produced results consistent with those of other techniques. Kleindienst et al. (2007) have recently extended the

CMB method to the *direct* estimation of SOA by using tracers identified in chamber studies, and report that SOA from isoprene, α-pinene, β-caryophyllene, and toluene accounted for 40% of the measured OC concentration at a suburban site in the southeastern USA. The advantage of the method is that it can be used for very large datasets covering a whole year and longer. However, the time resolution is rather low. The main disadvantage of these tracer methods is that the ratio of the tracer to organic mass needs to be constant for various conditions (low-NOₓ, high-NOₓ, at low and at high OA concentrations). One also needs to estimate/extrapolate the total SOA using low tracer concentrations (typically up to a couple of percent of the total SOA concentration).

### 4.2.2 ¹⁴C analysis and associated EC/OC tracer method

The analysis of ¹⁴C in atmospheric aerosols using accelerator mass spectrometry (Hellborg and Skog, 2008) allows the “age” of the organic carbon to be differentiated. ¹⁴C is a radioactive isotope of carbon with a half life of 5730 years. The combustion of fossil fuels, which are millions of years old, produces organic species with no ¹⁴C. Aerosols that have a modern carbon age contain material of biological origin, either biogenic SOA, biomass burning OA (either primary or secondary), primary biological particles, and also particles from anthropogenic sources such as meat cooking. Atomic bomb tests led to higher ¹⁴C in the atmosphere which result in different ¹⁴C levels in wood of different ages (Lewis et al., 2004). Usage of chemical markers such as levoglucosan, cellulose, sugars, or GC/MS analyzed VOC profiles is required to separate the different possible sources of modern carbon listed above.

The use of ¹⁴C analysis coupled with a discrimination of the carbon content into OC and EC has been used to determine that the EC fraction in Central Europe is generally dominated by fossil fuel (Szidat et al., 2006). It was also found that fossil-fuel combustion accounts for only ca. 30% of OC in Zürich, Switzerland, throughout the year, even in the city center (Szidat et al., 2006). Biomass burning in wintertime and SOA in summertime seems to account for the majority of the remaining OC. Results obtained at other sites in Switzerland are generally consistent with those from Zürich. The only exception to date, was the site at Roveredo, located in an Alpine valley, where more than 80% of the organic mass was estimated to originate from wood burning (Alfarra et al., 2007; Szidat et al., 2007; Sandradewi et al., 2008).

The EU CARBOSOL Project (Carbonaceous Aerosols over Europe, Legrand and Puxbaum, 2007; Pio et al., 2007) combined weekly measurements of EC, OC, inorganic ions, elemental composition, levoglucosan, cellulose and radioactive tracers across a network of six sites in southern central Europe. Gelencsér et al. (2007) combined all of these sources of information in an effort to calculate the relative contributions of the different primary and secondary sources of aerosol. Consistent with the results of Szidat et al. (2006),
<table><caption>**Table 3.** Selected marker compounds for identification of anthropogenic and biogenic sources of SOA. The marker compounds used to identify SOA from isoprene and α-/β-pinene are more extensively described in Sect. 5.1.</caption><thead><tr><th>Precursor type</th><th>Sources</th><th>Oxidation product types identified in SOA</th><th>Examples observed in ambient aerosol</th></tr></thead><tbody><tr><td colspan="4">Anthropogenic</td></tr><tr><td rowspan="3">Aromatics</td><td rowspan="3">Gasoline, Solvent usage</td><td>Cyclic anhydrides</td><td>furandione<sup>a</sup></td></tr><tr><td>Aromatic nitrophenols</td><td>5-methyl-2-nitrophenol</td></tr><tr><td>Dicarbonyls</td><td>methylglyoxal</td></tr><tr><td rowspan="2">Alkanes</td><td rowspan="2">Gasoline, Diesel</td><td>Carboxylic acids</td><td></td></tr><tr><td>Aldehydes</td><td>nonanal</td></tr><tr><td rowspan="3">Fatty Acids</td><td rowspan="3">Meat cooking</td><td>Carboxylic acids</td><td>decanoic acid</td></tr><tr><td>Furanones</td><td>dihydro-5-ethyl-2(3H)-furanone</td></tr><tr><td>Dicarboxylic acids</td><td>azelaic acid<sup>b</sup></td></tr><tr><td rowspan="2">Polycyclic Aromatic Hydrocarbons (PAHs)</td><td rowspan="2">Combustion</td><td>Esters</td><td>iso-propyl palmitate<sup>c</sup></td></tr><tr><td>Oxo-acids</td><td>9-oxononanoic acid</td></tr><tr><td rowspan="2">Plasticisers</td><td rowspan="2">Plastics</td><td>Oxygenated PAHs</td><td>9-fluoren-9-one</td></tr><tr><td>Nitrated PAHs</td><td>nitro-chrysene</td></tr><tr><td></td><td></td><td>Phthalates</td><td>dibutyl-phthalate</td></tr><tr><td colspan="4">Biogenic</td></tr><tr><td rowspan="3">Isoprene</td><td rowspan="3">Vegetation</td><td>Methyl tetrrols</td><td>2-methylthreitol<sup>d</sup></td></tr><tr><td>Carboxylic acids</td><td>2-hydroxymethylsuccinic acid</td></tr><tr><td>Organosulfates</td><td>2,3,4-trihydroxy-2-methylbutyl hydrogen sulfate</td></tr><tr><td rowspan="4">Monoterpenes</td><td rowspan="4">Vegetation</td><td>Aldehydes</td><td>2-methyltetrol sulfate esters<sup>e</sup></td></tr><tr><td>Carboxylic acids</td><td>pinonaldehyde<sup>f</sup></td></tr><tr><td>Organosulfates</td><td>pinonic acid</td></tr><tr><td>Oligomer</td><td>2,3-dihydroxypinane mixed nitrate/sulfate derivatives<sup>e</sup></td></tr><tr><td rowspan="2">Sesquiterpenes</td><td rowspan="2">Vegetation</td><td>Carboxylic acids</td><td>β-pinene dimer<sup>g</sup></td></tr><tr><td></td><td>β-caryophyllinic acid<sup>h</sup></td></tr></tbody></table>

it was found that wintertime residential wood burning was a significant contributor to measured OC levels at all CAR-BOSOL sites, whereas in summertime another modern-C source, likely BSOA, is the dominant contributor.

In Northern Europe studies have been much more limited, despite early attention from scientists. More than 20 years ago, the pioneering study of Currie et al. (1986) found that residential wood combustion accounted for on average 65% of carbon in fine aerosol at the town of Elverum 120 km north of Oslo during wintertime. Several recent studies have confirmed the dominance of wood combustion in wintertime for many areas (Yttri et al., 2005, 2009; Glasius et al., 2006; Hedberg et al., 2006; Hellén et al., 2008a; Saarikoski et al., 2008), although recent results from Gothenburg in Southern Sweden showed a lower wood-burning signal (Szidat et al., 2009). Very few summertime data are available, but Szidat et al. (2009) also found that Gothenburg showed a similar OC composition to that found for Zürich.

In the United States, a recent study found that modern carbon accounted for about 50% of carbon at 2 urban sites,

70–97% of carbon at 4 near-urban sites, and 80–100% of carbon at six remote sites, as annual averages (Schichtel et al., 2008). These findings are consistent with the earlier studies by Lewis et al. (2004) and Hildemann et al. (1994), and with the high fractions of biogenic SOC deduced from molecular marker methods (Kleindienst et al., 2007; Lewandowski et al., 2008).

In comparison to the marker method, <sup>14</sup>C is measured for the total organic carbon (or total carbon/water soluble organic carbon) and thus provides information directly about a larger fraction of the organic mass without the need for extrapolation from small tracer concentrations. However, the SOA fraction cannot be analyzed alone and one relies on OC/EC ratios of traffic and/or biomass burning sources. In the case of WSOC, the contributions of biomass burning need to be taken into account. As for the tracer method, the time resolution offered by <sup>14</sup>C analysis is low and the high costs associated with long measurement periods may be prohibitive.

<footnote><sup>a</sup> Hamilton et al. (2004), <sup>b</sup> Pol et al. (2006), <sup>c</sup> Goldstein et al. (2008), <sup>d</sup> Claeys et al. (2004a), <sup>e</sup> Surratt et al. (2008), <sup>f</sup> Williams et al. (2007), <sup>g</sup> Wozniak et al. (2008), <sup>h</sup> Lewandowski et al. (2007).</footnote>
### 4.2.3 Highly time-resolved studies

A number of recent studies have used the PILS-WSOC (Sullivan et al., 2004) and AMS (Canagaratna et al., 2007) techniques to quantify OM with a time resolution as low as minutes and seconds, respectively. These techniques have some ability to separate SOA from POA based on water-solubility or mass spectral patterns, and the identification of SOA by both methods has been shown to be very similar in Tokyo (Kondo et al., 2007), although recent results suggest that in some environments the non-WSOC fraction of SOA can be very significant (Favez et al., 2008). These methods allow correlation with time-series of other atmospheric tracers also collected at high resolution, resulting in lower uncertainty in the identification of the OA components and the quantification of POA emission or SOA formation ratios (e.g., POA/CO, SOA/(NO₂+O₃)).

De Gouw et al. (2005) used AMS data from the NEAQS 2002 ship-based study off the coast of the Eastern USA to show that OA/C₂H₂ had low values in fresh emissions and grew tenfold due to photochemical processing within a timescale of one day, clearly showing that SOA formation in these polluted airmasses quickly overwhelmed POA emissions. The formed SOA correlated strongly with tracers of anthropogenic pollution such as isopropyl nitrate, and did not correlate with the available biogenic tracers, despite the expectation of a large contribution of BSOA to OA levels in this region (Chen et al., 2006). These results have been largely confirmed by the analysis of the more extensive dataset from the ICARTT study (de Gouw et al., 2008). Volkamer et al. (2006) extended the rapid formation of SOA to very short timescales in the urban environment and showed that a state-of-the-art SOA model underpredicted the observed SOA by almost an order of magnitude after a few hours of photochemistry. Weber et al. (2007) found a large biogenic fraction of organic carbon and especially of its water-soluble fraction using ¹⁴C analysis, in line with other studies, which appears to be in contradiction to the high correlation with anthropogenic tracers observed in the same study. One possible explanation for this discrepancy is that SOA formation from biogenic VOC was greatly enhanced by anthropogenic emissions. To settle this question, methods to distinguish anthropogenic and biogenic SOA that allow for a higher time resolution than ¹⁴C analysis would be highly beneficial.

Zhang et al. (2005a, b) used AMS data from Pittsburgh to develop the first quantification of OA components using the full AMS organic spectrum, and showed that two prominent components accounted for most of the mass in this study. Oxygenated organic aerosol (OOA), a component with a mass spectrum typical of highly oxygenated species, was identified as a surrogate for SOA and correlated strongly with sulfate, a secondary inorganic tracer. OOA was observed to be formed in a relatively constant proportion to

ozone during a case study dominated by photochemistry, similar to results from Mexico City (Volkamer et al., 2006; Herndon et al., 2008). Hydrocarbon-like organic aerosol (HOA) had a spectrum typical of reduced species (hydrocarbons or species with a hydrocarbon backbone) and correlated strongly with combustion tracers (CO, NOₓ, EC), and was considered a surrogate for POA. OOA was larger than HOA at these urban locations, consistent with a dominance of highly polar species followed by non-polar species observed in Pittsburgh using FTIR spectroscopy (Polidori et al., 2008). Zhang et al. (2005b) illustrate the main limitation of the EC tracer method, namely that the typical method of determining (OC/EC)ₚri from ambient data during periods of suspected "dominant" POA influence can lead to large positive biases due to the prevalence of a significant SOA background. Zhang et al. (2007b) applied an improved multicomponent spectral analysis method (MCA) to 37 AMS datasets from the polluted regions of the Northern Hemisphere, and confirmed the ubiquity and importance of OOA even in urban areas and its overwhelming dominance in rural and remote sites. Lanz et al. (2007) applied the positive matrix factorization (PMF) method to AMS spectra and largely confirmed the results of Zhang et al. (2005a, b), for a dataset in Zürich, while providing more detail on the POA and SOA components. In this study, it was shown that OOA contained two different components, a low volatility fraction (OOA1) highly correlated with sulfate and a fraction of higher volatility (OOA2) correlated with nitrate with enhanced concentrations during the night (due to lower temperatures than daytime). Similar OOA1 and OOA2 components correlating with sulfate and nitrate, respectively, were also found in Pittsburgh (Ulbrich et al., 2009). DeCarlo et al. (2008) reported a rapid gain of oxygen by OOA in the outflow of Mexico City, with the atomic O/C of OA reaching ~0.8 after 1 day of photochemistry. Also in wintertime, Lanz et al. (2008) found that OOA (probably mostly SOA) contributed to more than 50% of OA present in Zürich. In this case, no split into OOA1 and OOA2 occurred, and the highest correlation was found with the sum of nitrate and sulfate. OOA2 did not show a temporally varying aerosol partitioning under these winter conditions. The combination of these AMS results with ¹⁴C analyses (Szidat et al., 2006) reveals that both in summer and winter, SOA is mostly non-fossil in origin. In summer, this could be due to the oxidation of biogenic emissions (terpenes and isoprene) but in winter it is most likely that other sources, such as the oxidation of gaseous emissions from wood burning, make an important contribution (Lanz et al., 2008). Indeed, one might expect that the semivolatiles present in wood burning emissions could form SOA with a similar efficiency as those present in diesel emissions (Robinson et al., 2007; Grieshop et al., 2009). The work of Weimer et al. (2008) adds some complexity to these issues by showing that emissions from one type of wood burning stove can be similar to OOA. Although these results do not appear to be representative of wildfire emissions, additional evidence
should be used to attribute OOA to SOA when the influence of wood smoke is significant.

The major advantages of using AMS data are the high time and size resolution and that analysis of the whole organic mass is performed. The latter reduces the possible errors that are inferred by the extrapolation from tracer concentrations. The combination of AMS data with the ¹⁴C analyses even allows the distinction of SOA from fossil and non-fossil precursors. However, the statistical analysis of the AMS data assumes a constant mass spectrum for the different sources over a given time. Most analyses cover only a couple of weeks, the duration of a field campaign, because the deployment of such an instrument over a long time period, e.g., one year, is rather labor intensive and expensive. Finally as mentioned, the separation and attribution to sources need to be performed carefully. So far, SOA from different sources could not be distinguished. As already mentioned, OOA might not always represent SOA because of possible contributions of wood burning and also by heterogeneous oxidation of primary particles.

In summary, the study of SOA in the ambient atmosphere is complicated because it is usually not separated from other components. New methods for the quantification of the various organic components are still needed in this field. At present, the most suitable approach is to combine the available methods during field campaigns to allow for a more complete analysis than with just one method at the time.

## 4.3 Modeling studies

There have been a number of important developments in the tools and parameterizations used for atmospheric modeling since the review of Kanakidou et al. (2005). The modeling changes include (i) large revisions in understanding of SOA yields brought about by newer experimental procedures, (ii) a suggested new framework (the VBS approach discussed above), better suited to tackling the complexities of SOA formation, reaction, and volatility, and (iii) advances in the understanding of thermodynamics of mixed inorganic-organic systems. In addition, there has been a significant increase in the quantity and quality of ambient measurements available with which to evaluate both models and emission inventories.

### 4.3.1 Model approaches

In principle, models of SOA formation need to represent oxidation reactions of gas-phase organic species, which lead to compounds of lower volatility through addition of functional groups, but can also yield products of higher volatility by cleavage of carbon-carbon bonds. In addition, models need to include reactions of the semivolatile species in the particle phase, which tend to give products of even lower volatility. To date, two approaches to representing SOA formation have been largely used in atmospheric models:

1. Empirical models constrained by laboratory data. The two-product gas-aerosol partitioning model, with parameters derived from laboratory data and with extrapolation to different temperatures using an assumed (or measured) *effective* value of the latent heat of vaporization of the SOA.

2. Explicit and semi-explicit models in which gas-phase mechanisms predict formation of semivolatile products, with gas-particle partitioning computed from explicit calculation of $K_p^i$ for each semivolatile compound depending on the composition of the organic aerosol absorbing mass.

The simpler Odum-type models, with laboratory-derived values of $\alpha_i$ and $K_p^i$ have been incorporated into atmospheric chemical transport and general circulation models for a number of years (e.g., Andersson-Sköld and Simpson, 2001; Schell et al., 2001; Chung and Seinfeld, 2002; Koo et al., 2003; Pun et al., 2003; Tsigaridis and Kanakidou, 2003; Heald et al., 2005; Henze and Seinfeld, 2006; Lane et al., 2007; Simpson et al., 2007; Henze et al., 2008). These models are simple enough to be implemented for global scale modeling of SOA. However, this simple approach cannot account for the complexity and dynamics of SOA systems, so alternative approaches are likely in next-generation models.

The most complex of the explicit models include thousands of reactions, such as the Master Chemical Mechanism (Jenkin et al., 2004; Johnson et al., 2004, 2005, 2006), or NCAR Self-Generating Mechanism (Aumont et al., 2005; Camredon et al., 2007). These schemes can account for multiple generations of oxidation and the progression of the volatility distribution as the generations unfold. Although no chemical transport model currently treats particle-phase reactions explicitly, these can be included as soon as a reasonable mechanism becomes available, and indeed some box-model simulations (for smog chambers) have begun exploring the importance of such reactions (Li et al., 2007; Capouet et al., 2008). More compact explicit model approaches (or surrogate schemes) have been designed for inclusion in 3-D atmospheric models, including CACM and derivatives (Griffin et al., 2002a, b, 2003, 2005; Pun et al., 2002; Vutukuru et al., 2006; Chen et al., 2006, 2007), EMEP/Kam2(X) (Andersson-Sköld and Simpson, 2001; Simpson et al., 2007) and ORILAM-SOA (Tulet et al., 2006).

Challenges associated with implementing explicit and semi-explicit models include: (a) Establishing the basic science of the detailed gas-phase oxidation chemistry and resulting semivolatile products; (b) Determining accurate values of saturation concentrations (vapor pressures) for the semivolatile compounds of interest; (c) Calculating $K_p^i$ values that account for the molecular properties (activity coefficients, etc.) of the absorbing organic/water mixture; and (d) Determining particle-phase reactions of importance. Each of the current explicit models deals with these challenges to
a different extent. Since it is unlikely that all of the challenges listed above will be overcome to such an extent that explicit models can be used without experimental verification (e.g., Clegg et al., 2008a, b), laboratory chamber data must continue to serve as the fundamental basis that constrains all SOA models, followed by evaluation using field data.

Most SOA modeling has focused on the partitioning of organic products onto pre-existing nonacidic particles or by nucleation, leading to new particles. Until now, only a few SOA models have considered heterogeneous or multiphase reactions (Johnson et al., 2005; Ervens et al., 2008), and the existing SOA models are not feasible for use with heterogeneous reactions involving neutral to highly acidic aerosol. Jang et al. (2006a) recently developed a predictive SOA model that unites particle-phase heterogeneous reactions and the gas-phase kinetics of precursor VOCs with the thermodynamic processes of organic and inorganic species. The predictive SOA model takes into account both partitioning and heterogeneous reactions. The SOA mass resulting from heterogeneous reactions (*OM*<sub>H</sub>) is estimated using the concentration of organic compounds partitioned into the inorganic layer and heterogeneous rate constants expressed by a semi-empirical model (Jang et al., 2005), which considers particle acidity and molecular structures. This *OM*<sub>H</sub> model is incorporated into the modified partitioning SOA model originally developed by Schell et al. (2001), and has been used to interpret smog chamber experiments for α-pinene. The importance of acidity for both smog chamber and atmospheric modeling is still very unclear however (Kroll and Seinfeld, 2008) and it is not apparent in field data (Zhang et al., 2007b; Peltier et al., 2007a), and no regional or global models have attempted to include such parameterizations.

The VBS approach offers an attractive framework for SOA models that lie intermediate between the two-parameter Odum model and fully explicit models. In the VBS approach, oxidation of the parent hydrocarbon leads to an array of semivolatile products, which evolve through several generations of reactions (Donahue et al., 2006). The gas-particle distribution of the semivolatile products evolves as the amount of aerosol in each volatility bin increases; the SOA yield at any time is then the summation of condensed-phase concentrations in each of the volatility bins. Rates of oxidation can be accounted for reasonably well based on established rate constants, and product volatilities, as reflected by *C*\* values, can be determined by fitting the model to laboratory data (Lane et al., 2008; Shrivastava et al., 2008). The importance of volatility with respect to so-called primary emissions was demonstrated with the VBS approach by Robinson et al. (2007), who highlighted the link between anthropogenic emissions of semi- and intermediate volatility precursors and the oxygenated organic aerosol (OOA) observed by AMS instruments. The challenges facing the VBS framework are to obtain accurate coupling parameters and to determine the appropriate number of bins (for example with different oxygen to carbon ratios, polarities, or water solubi-

lities) for lumped compounds in large-scale models and how to account for changing chemical regimes when specifying yields of different product distributions.

### 4.3.2 Model predictions of SOA formation vs. ambient data

Long-term measurements of OC in the atmosphere are now available from a number of sites in the USA and Europe (e.g., IMPROVE network in the USA, the EMEP EC/OC campaign and EUSAAR in Europe). Most comparisons between modeled and observed levels of OC at such sites suffer from the fact that the origin of the measured OC is unknown – typically with no information on the relative contributions of primary or secondary organics, or of anthropogenic or biogenic sources. In many cases discrepancies may be due to problems with emission inventories, especially in wintertime where wood-burning contributions can dominate ambient OC levels at the surface in populated areas. Some recent studies though have suggested that there are indeed problems in predicting SOA in the atmosphere with current models (de Gouw et al., 2005; Heald et al., 2005; Volkamer et al., 2006; Kleinman et al., 2008). Even the most complex chemical schemes display problems: in the study of SOA formation in the UK (Johnson et al., 2006) all partitioning coefficients in the modified Master Chemical Mechanism (MCMv3.1) had to be increased by a species-independent factor of 500 in order to capture observed OA levels, likely reflecting the need for further volatility-reducing processes in the mechanism, or missing SOA precursors. The recent study of Simpson et al. (2007) also showed a significant (factor 3–5) underprediction of SOA levels for sites in central-southern Europe (as derived using ¹⁴C, levoglucosan, cellulose and EC/OC ratios by Gelencsér et al., 2007). However, the same model showed a very good agreement with total carbon levels at northern European sites, illustrating that the extent of model-measurement agreement may also depend on where and when a model is applied, and not just upon its formulation. Model evaluations using total carbon may also hide SOA underestimation if the POA is overestimated (Zhang et al., 2007a).

The recent review from Kroll and Seinfeld (2008) discusses potential reasons for model-measurement discrepancies in detail. Potential factors include actual differences in chemistry between chamber experiments and the atmosphere, incomplete treatment of SOA chemistry in models, and omission of important (unrecognized) precursors in models. Even for those compounds that are known (or suspected) to be important in SOA formation, critical information is missing. Indeed, for many, if not most, of the identified SOA compounds, measured vapor pressures are unavailable, and one must employ a semi-empirical model to estimate vapor pressures. Several such methodologies exist (e.g., Asher et al., 2002; Asher and Pankow, 2006; Camredon and Aumont, 2006), but uncertainties for specific compounds
can cover several orders of magnitude (Clegg et al., 2008a). The importance of these uncertainties was demonstrated by the Simpson et al. (2007) study which made use of two sets of vapor pressure estimates for BSOA formation, both sets derived from a combination of theory and chamber experiments. These two simulations gave dramatic differences in modeled BSOA and OC estimates. Difficulties with other thermodynamic quantities are discussed further in Sect. 5.7. Among the potentially important precursors, much attention has been devoted recently to “intermediate volatility” and semivolatile organics, such as those in diesel exhaust and biomass burning (Robinson et al., 2007; Weitkamp et al., 2007; Grieshop et al., 2009). Indeed, Chan et al. (2009) recently demonstrated that polycyclic aromatic hydrocarbons found in diesel exhaust can produce SOA upon photooxidation under low- and high-NOₓ conditions. Aqueous pathways are also possible (Warneck, 2003; Carlton et al., 2006; Ervens et al., 2008), but very uncertain. There is also strong evidence for the presence of an ambient pool of hydrocarbons that is largely unaccounted for in models (Lewis et al., 2000; Di Carlo et al., 2004; Holzinger et al., 2005).

# 5 Current and emerging issues in SOA research

## 5.1 Molecular characterization of biogenic SOA constituents

Molecular characterization of organic aerosol constituents is important because it allows one to gain insights into aerosol sources and the underlying mechanisms of SOA formation and transformation (or aging). Common terms to denote analytically accessible organic compounds that provide this valuable information in a complex matrix such as ambient aerosol are “tracer” or “marker”. During the past few years considerable progress has been made in the molecular characterization of individual SOA constituents from the photooxidation of isoprene, α-/β-pinene and other selected BVOCs that can serve as tracers for organic aerosol characterization.

### 5.1.1 SOA tracers for the photooxidation of isoprene

The chemical structures and names of isoprene SOA constituents that have recently been discovered are listed in Table 4.

## 2-methyltetrols, 2-methylglyceric acid, C₅-alkene triols, and related oligomeric products

In the case of isoprene, identified SOA tracers are 2-methyltetrols (i.e., the diastereoisomers, 2-methylthreitol and 2-methylerythritol), 2-methylglyceric acid and three isomeric C₅-alkene triols identified in field samples from the Amazon basin, Brazil, and from a mixed deciduous/coniferous forest site at K-puszta, Hungary. GC/MS with prior trimethylsilylation was used for their analysis, chemical

structures were proposed based on EI and methane CI MS data, and confirmed through synthesis of authentic reference compounds (Claeys et al., 2004a, b; Wang et al., 2004).

Their discovery by Claeys et al. (2004a, b) came as a great surprise to the atmospheric science community because it was previously thought that the photooxidation of isoprene did not result in SOA. Laboratory experiments with irradiated isoprene/NOₓ/air mixtures in the presence and absence of SO₂ (which is oxidized to sulfuric acid) confirmed that both the 2-methyltetrols and 2-methylglyceric acid are formed by oxidation of isoprene (Edney et al., 2005). In addition, this study, as well as a recent study by Surratt et al. (2007b), revealed that sulfuric acid is crucial in their formation and results in an enhanced SOA yield and in increased 2-methyltretol concentrations. Additional insights into the mechanism leading to the 2-methyltetrols were obtained in a laboratory study by Böge et al. (2006). They concluded that the OH radical/O₂ reaction route of isoprene involving permutation reactions of peroxy radicals can be important only in low-NOₓ environments. In addition, they proposed a second mechanism leading to the formation of the 2-methyltetrols that holds for high-NOₓ conditions.

Surratt et al. (2006) then investigated the chemical composition of SOA from the photooxidation of isoprene over a full range of NOₓ levels and seed aerosol conditions through a series of controlled laboratory chamber experiments. SOA composition was studied using a wide range of complementary experimental techniques, including mass spectrometric methods and an iodometric-spectrometric method. Formation of higher-MW products was observed to be an important SOA formation pathway in all cases; however, the nature of these products was found to be strongly dependent on the NOₓ level. Under low-NOₓ conditions, organic peroxides contributed significantly to the SOA mass and decreased with time, indicating photochemical aging. Major products identified were 2-methyltetrols and C₅-alkene triols, indicating that these compounds can serve as suitable tracers for the photooxidation of isoprene under low-NOₓ conditions. Minor products that could be tentatively identified included 2-methyltretol oxyformate derivatives, C₅-trihydroxy monocarboxylic acids and hemiacetal dimers formed between 2-methyltetrols and a C₅-dihydroxycarbonyl. Interestingly, the latter higher-MW products were also found in fine (PM₂.₅) aerosol collected from the Amazonian rainforest, demonstrating the atmospheric relevance of the low-NOₓ chamber experiments. The major products identified in the high-NOₓ SOA were acidic oligoesters with 2-methylglyceric acid as a key monomeric unit and residues formed by esterification of hydroxyl groups with acetic and/or formic acid (Surratt et al., 2006; Szmigielski et al., 2007a). Jaoui et al. (2008) recently observed the diester formed from the esterification of two 2-methylglyceric acid residues in ambient aerosol collected from the Southeastern USA, thus providing a tracer for SOA formation from isoprene photooxidation under high-NOₓ conditions.
<table><caption>**Table 4.** Isoprene SOA constituents discovered since 2004: chemical structures, names and derivatives; laboratory studies in which isoprene SOA tracers were investigated, and specific conditions; and field studies in which the tracers have been identified or detected, and, in some cases, also quantified.</caption><thead><tr><th rowspan="2">Chemical structure<br>trivial name (IUPAC name)<br>derivatives</th><th colspan="4">Laboratory studies: reaction conditions, reference</th><th rowspan="2">Field studies: site; type of aerosol [PM<sub>1</sub>, PM<sub>2.5</sub>, PM<sub>10</sub>, size-fractionated aerosol (SFA), or total aerosol (TA)]; reference</th></tr><tr><th>VOC conc.</th><th>oxidant<sup>b</sup></th><th>seed aerosol /<br>atomizing solution</th><th>reference</th></tr></thead><tbody><tr><td>• 2-methyltretol<br>(2-methyl-1,2,3,4-tetrahydroxy-<br>butane)</td><td>- 1680 / 1610 ppb<br>isoprene</td><td>- 647 / 648 ppb NO<br>- 629 / 627 ppb NO<sub>x</sub><br>- 268 / &lt; 1 ppb SO<sub>2</sub></td><td>- (NH<sub>4</sub>)<sub>2</sub>SO<sub>4</sub></td><td>- Edney et al.<br>(2005)</td><td>- Balbina, Brazil; PM<sub>2.5</sub> and TA; Claeys et al. (2004a)<br>- K-puszta, Hungary; PM<sub>2.5</sub>; Ion et al. (2005)<br>- southeastern USA; PM<sub>2.5</sub>; Edney et al. (2005)<br>- Hyytiälä, Finland; PM<sub>1</sub>; Kourtchev et al. (2005; 2008a)</td></tr><tr><td>2-methylthreitol (2R,3R)<br>+ 2S,3S stereoisomer<sup>a</sup></td><td>- 5 ppm isoprene<br>- 1.5 ppm 2-methyl-2-vinyloxirane or<br>2-methyl-3-butene-1,2-diol</td><td>- 1.5 ppm H<sub>2</sub>O<sub>2</sub> //<br>without<br>photooxidation</td><td>- 30 / 50 mM<br>(NH<sub>4</sub>)<sub>2</sub>SO<sub>4</sub> / H<sub>2</sub>SO<sub>4</sub></td><td>- Böge et al.<br>(2006)</td><td>- Rondônia, Brazil; PM<sub>2.5</sub>; Schkolnik et al. (2005)<br>- Melpitz, Germany; PM<sub>1</sub>, PM<sub>2.5</sub>, PM<sub>10</sub>; Böge et al. (2006)<br>- Blodgett forest, CA, USA; TA; Cahill et al. (2006)<br>- Rondônia, Brazil; PM<sub>2.5</sub>; Decesari et al. (2006)<br>- Fichtelgebirge, Germany; PM<sub>2.5</sub>; Plewka et al. (2006)<br>- upstate New York, USA; PM<sub>2.5</sub>; Xia and Hopke (2006)</td></tr><tr><td>2-methylerythritol (2S,3R)<br>+ 2R,3S stereoisomer<sup>a</sup></td><td>- 100 or 500 ppb<br>isoprene</td><td>- 3 ppm H<sub>2</sub>O<sub>2</sub> //<br>low-NO<sub>x</sub></td><td>- 15 mM (NH<sub>4</sub>)<sub>2</sub>SO<sub>4</sub><br>- 15 / 15 mM<br>(NH<sub>4</sub>)<sub>2</sub>SO<sub>4</sub> / H<sub>2</sub>SO<sub>4</sub></td><td>- Surratt et al.<br>(2006)</td><td>- southeastern USA; PM<sub>2.5</sub>; Clements and Seinfeld (2007)<br>- southeastern USA; PM<sub>2.5</sub>; Lewandowski et al. (2007)<br>- Rondônia, Brazil; PM<sub>2.5</sub> and SFA; Fuzzi et al. (2007)</td></tr><tr><td>• oxyformate derivatives</td><td>- see 2-methyltretol</td><td></td><td></td><td>- Surratt et al.<br>(2006)</td><td></td></tr><tr><td>• hemiacetal dimers with a C<sub>5</sub>-<br>dihydroxycarbonyl</td><td>- see 2-methyltretol</td><td></td><td></td><td>- Surratt et al.<br>(2006)</td><td>- Rondônia, Brazil; PM<sub>2.5</sub>; Surratt et al. (2006)</td></tr><tr><td>• sulfate and mixed nitrate/sulfate<br>derivatives</td><td>- 500 ppb isoprene</td><td>- 3 ppm H<sub>2</sub>O<sub>2</sub> //<br>low-NO<sub>x</sub></td><td>- 15 mM (NH<sub>4</sub>)<sub>2</sub>SO<sub>4</sub><br>- 15 / 15 mM<br>(NH<sub>4</sub>)<sub>2</sub>SO<sub>4</sub> / H<sub>2</sub>SO<sub>4</sub></td><td>- Surratt et al.<br>(2007a)</td><td>- southeastern USA; PM<sub>2.5</sub>; Surratt et al. (2007a; 2008)<br>- K-puszta, Hungary; PM<sub>2.5</sub>; Gómez-González et al. (2008)</td></tr><tr><td></td><td>- 500 ppb isoprene</td><td>- 3 ppm H<sub>2</sub>O<sub>2</sub> /<br>891-963 ppb NO<sub>x</sub><br>// high-NO<sub>x</sub></td><td>- 15 mM (NH<sub>4</sub>)<sub>2</sub>SO<sub>4</sub><br>- 15 / 15 mM<br>(NH<sub>4</sub>)<sub>2</sub>SO<sub>4</sub> / H<sub>2</sub>SO<sub>4</sub></td><td></td><td></td></tr><tr><td></td><td>- 500 ppb isoprene</td><td>- HONO / 366-382<br>ppb NO<sub>x</sub> // high-NO<sub>x</sub></td><td>- 15 mM (NH<sub>4</sub>)<sub>2</sub>SO<sub>4</sub></td><td></td><td></td></tr><tr><td></td><td>- 2500 ppb<br>isoprene</td><td>- 200 ppb NO<sub>x</sub></td><td>- 15 / 15 mM<br>(NH<sub>4</sub>)<sub>2</sub>SO<sub>4</sub> / H<sub>2</sub>SO<sub>4</sub><br>- 0.92 mM H<sub>2</sub>SO<sub>4</sub></td><td></td><td></td></tr><tr><td></td><td>- 1598 ppb<br>isoprene</td><td>- 475 ppb NO<sub>x</sub></td><td>- 60 ppb SO<sub>2</sub><br>- 200 ppb SO<sub>2</sub></td><td></td><td></td></tr><tr><td>• sulfate derivatives of hemiacetal<br>dimers with a C<sub>5</sub>-dihydroxycarbonyl</td><td>- 1598 ppb<br>isoprene</td><td>- 475 ppb NO<sub>x</sub></td><td>- 60 ppb SO<sub>2</sub><br>- 200 ppb SO<sub>2</sub></td><td>- Surratt et al.<br>(2008)</td><td>- southeastern USA; PM<sub>2.5</sub>; Surratt et al. (2008)</td></tr><tr><td>• 2-methylglyceric acid<br>(2,3-dihydroxy-2-methylpropanoic<br>acid)</td><td>- 2-methyl-2-vinyloxirane</td><td>- H<sub>2</sub>O<sub>2</sub> / H<sub>2</sub>O</td><td>- none</td><td>- Claeys et al.<br>(2004b)</td><td>- K-puszta, Hungary; PM<sub>2.5</sub>; Claeys et al. (2004b)<br>- K-puszta, Hungary; PM<sub>2.5</sub>; Ion et al. (2005)<br>- southeastern USA; PM<sub>2.5</sub>; Edney et al. (2005)<br>- southeastern USA; PM<sub>2.5</sub>; Yu et al. (2005)</td></tr><tr><td></td><td>- see 2-methyltretol</td><td></td><td></td><td>- Edney et al.<br>(2005)</td><td>- southeastern USA; PM<sub>2.5</sub>; Lewandowski et al. (2007)<br>- Jülich, Germany; PM<sub>2.5</sub>; Kourtchev et al. (2008b)</td></tr><tr><td></td><td>- 500 ppb isoprene<br>or methacrolein</td><td>- 3 ppm H<sub>2</sub>O<sub>2</sub> /<br>850-963 ppb NO<sub>x</sub><br>// high-NO<sub>x</sub></td><td>- none<br>- 15 mM (NH<sub>4</sub>)<sub>2</sub>SO<sub>4</sub><br>- 15 / 15 mM<br>(NH<sub>4</sub>)<sub>2</sub>SO<sub>4</sub> / H<sub>2</sub>SO<sub>4</sub></td><td>- Surratt et al.<br>(2006)<br>- Szmigielski et al.<br>(2007a)</td><td></td></tr><tr><td></td><td>- 500 ppb isoprene</td><td>- HONO / 366-382<br>ppb NO<sub>x</sub> // high-NO<sub>x</sub></td><td>- none<br>- 15 mM (NH<sub>4</sub>)<sub>2</sub>SO<sub>4</sub></td><td></td><td></td></tr><tr><td>• oligoesters (n = 2-4) and<br>corresponding nitrate, acetate and<br>formate derivatives and mixed<br>derivatives thereof</td><td>- see 2-methylglyceric<br>acid</td><td></td><td></td><td>- Surratt et al.<br>(2006)<br>- Szmigielski et al.<br>(2007a)</td><td>- southeastern USA; PM<sub>2.5</sub>; Jaoui et al. (2008)</td></tr><tr><td>• sulfate derivative</td><td>- see high NO<sub>x</sub><br>conditions for<br>sulfate and mixed<br>nitrate/sulfate<br>derivatives</td><td></td><td></td><td>- Surratt et al.<br>(2007a)</td><td>- southeastern USA; PM<sub>2.5</sub>; Surratt et al. (2007a; 2008)<br>- K-puszta, Hungary; PM<sub>2.5</sub>; Gómez-González et al. (2008)</td></tr></tbody></table>
<table><caption>**Table 4. Continued.**</caption><thead><tr><th></th><th></th><th></th><th></th><th></th><th></th></tr></thead><tbody><tr><td>• C<sub>5</sub>-alkene triols<br>(Z-2-methyl-1,3,4-trihydroxybut-1-ene)<br>(E-2-methyl-1,3,4-trihydroxybut-1-ene)<br>(3-methyl-2,3,4-trihydroxybut-1-ene)</td><td>- see 2-methyltetrrol</td><td></td><td>- Surratt et al. (2006)</td><td>- Rondônia, Brazil; PM<sub>2.5</sub>; Wang et al. (2005)<br>- K-puszta, Hungary; PM<sub>2.5</sub>; Ion et al. (2005)<br>- Hyytiälä, Finland; PM<sub>1</sub>; Kourtchev et al. (2005; 2008a)<br>- Blodgett forest, CA, USA; TA; Cahill et al. (2006)<br>- Jülich, Germany; PM<sub>2.5</sub>; Kourtchev et al. (2008b)</td></tr><tr><td>• glyoxal (ethanedione) and methylglyoxal (propane-1,2-dione) sulfate derivatives<br>glyoxal sulfate<br>methylglyoxal sulfates (2 isomers)</td><td>- 3.6-5.4 ppb glyoxal<br>- see high NO<sub>x</sub> conditions for sulfate and mixed nitrate/sulfate derivatives</td><td>- none</td><td>- 24mM (NH<sub>4</sub>)<sub>2</sub>SO<sub>4</sub><br>- 24 / 38mM (NH<sub>4</sub>)<sub>2</sub>SO<sub>4</sub> / H<sub>2</sub>SO<sub>4</sub><br>- 25 / 113mM (NH<sub>4</sub>)<sub>2</sub>SO<sub>4</sub> / H<sub>2</sub>SO<sub>4</sub></td><td>- Liggio et al. (2005a,b)<br>- Surratt et al. (2007a)</td><td>- southeastern USA; PM<sub>2.5</sub>; Surratt et al. (2007a; 2008)<br>- K-puszta, Hungary; PM<sub>2.5</sub>; Gómez-González et al. (2008)</td></tr><tr><td>• C<sub>5</sub>-alkane triol (1,2,3-trihydroxy-2-methyl-butane) mixed nitrate/sulfate derivative<br>+ positional isomer</td><td>- see sulfate derivatives of hemiacetal dimers with a C<sub>5</sub>-dihydroxy-carbonyl</td><td></td><td></td><td>- Surratt et al. (2008)</td><td>- southeastern USA; PM<sub>2.5</sub>; Surratt et al. (2008)</td></tr></tbody></table>

a structure of stereoisomer not shown; b H<sub>2</sub>O<sub>2</sub>, HONO and NO<sub>x</sub> serve as OH radical sources in photooxidation experiments.

Importantly, most of the isoprene SOA tracers discussed above have been measured in several field studies conducted at forested sites on three different continents (see Table 4). With regard to the 2-methyltetrrols, the atmospheric concentrations during summer in forested sites were in the range 0.02–365 ng m<sup>-3</sup>, with the highest concentrations found in Research Triangle Park (NC, USA), a suburban site in the Southeastern USA. 2-methyltetrrols show a diel variation with the highest concentrations during day-time, consistent with their formation from isoprene which is light- and temperature-dependent (Ion et al., 2005; Kourtchev et al., 2008a). As expected, the 2-methyltetrrols follow a seasonal trend with the highest concentrations during the warmest months (Xia and Hopke, 2006).

## Organosulfates and nitrooxy organosulfates from isoprene SOA

Romero and Oehme (2005) and Reemtsma et al. (2006) first reported organosulfates in ambient aerosol based on the use of (–)ESI-MS; however, their sources and source processes remained unclear. Surratt et al. (2007a, b) then detected organosulfates, as well as nitrooxy organosulfates from isoprene SOA products and showed that they are formed by esterification of hydroxyl groups or keto groups (after gem-diol

formation) with sulfuric acid (Table 4). Organosulfates and nitrooxy organosulfates should be regarded as processed (or aged) products because their formation involves the participation of sulfuric acid, and in the case of nitrooxy organosulfates also that of NO<sub>x</sub>. Both sulfuric acid and NO<sub>x</sub> are mainly anthropogenic in origin, resulting in the organosulfates and nitrooxy organosulfates of isoprene having a mixed biogenic/anthropogenic origin. Isoprene organosulfates and nitrooxy organosulfates were detected at sites in the southeastern USA and Europe (Surratt et al., 2007a, 2008; Gómez-González et al., 2008). Major organosulfates formed from isoprene SOA detected in ambient aerosol include derivatives of the 2-methyltetrrols, glyoxal and methylglyoxal.

It is suggested that organosulfates and nitrooxy organosulfates, which are very polar acidic products, correspond to a substantial fraction of the humic-like substances of ambient aerosol. Considering their very polar character, they may enhance the capacity of ambient aerosol to act as cloud condensation nuclei. Quantitative data on organosulfates and nitrooxy organosulfates from the photooxidation of isoprene are not available yet. Considering that the 2-methyltetrrols may be present as sulfate, nitrate and mixed sulfate/nitrate derivatives in ambient aerosol, it is likely that these derivatives are measured in their hydrolyzed form (i.e., containing hydroxyl groups instead of sulfate and/or nitrooxy groups)
when trimethylsilylation GC/MS is employed (Surratt et al., 2007a), owing to the fact that the trimethylsilylation reagent contains an acidic catalyst. Analytical methods need to be developed that allow the simultaneous measurement of isoprene SOA products in their original forms (i.e., comprising the free non-esterified forms as well as organosulfates and nitrooxy organosulfates).

Previous laboratory studies have employed high levels of seed aerosol acidity, which in most cases are not atmospherically relevant, when oxidizing isoprene and other BVOCs to generate SOA. Since a large number of organosulfates and nitrooxy organosulfates have been observed in ambient aerosol, it is expected that organosulfate formation is likely dependent upon the sulfate aerosol mass concentration; however, the form of this dependency remains unclear. Surratt et al. (2007a) found that organosulfates and nitrooxy organosulfates of isoprene could form from non-acidified sulfate seed aerosol; however, the number of organosulfate and nitrooxy organosulfate products increased with increasing sulfuric acid concentration in the atomization solution. It is suggested that further work be done in understanding whether sulfate aerosol mass concentration, level of acidity, ionic strength, or all of these factors, affect the organosulfate formation potential from isoprene (as well as from other BVOCs) and whether other oxidants, such as $O_3$ and/or $NO_3$, can produce organosulfates of isoprene, as well as other tracer compounds. Recent work by Ng et al. (2008) has shown that the $NO_3$-initiated oxidation of isoprene in the presence of non-acidified and acidified sulfate seed aerosol can also yield organosulfates and nitrooxy organosulfates of isoprene.

### 5.1.2 SOA tracers for the photooxidation of α/-β-pinene

The chemical structures and names of α-pinene SOA constituents that have recently been elucidated are listed in Table 5.

#### 3-hydroxyglutaric acid and 3-methyl-1,2,3-butanetrichloroxylic acid

In the case of α- and β-pinene, it has been well established that both *cis*-pinic and *cis*-pinonic acids are major ozonolysis products, which are also produced in reactions with the OH radical. However, chemical analyses of irradiated α- or β-pinene/$NO_x$ mixtures that involve reactions with OH, as well as with ozone, reveal the presence of highly oxidized, acyclic, polar compounds, along with the ozonolysis products (Jaoui et al., 2005). Since these highly oxidized compounds were also observed in the fine fraction of ambient PM samples, considerable efforts have been undertaken to elucidate the chemical structures of the α/-β-pinene SOA compounds with MWs 148 and 204. The MW 148 compound was elucidated as the $C_5$-hydroxydicarboxylic acid 3-hydroxyglutaric acid (Claeys et al., 2007), while the

MW 204 compound was established as the $C_8$-tricarboxylic acid 3-methyl-1,2,3-butanetricarboxylic acid (Szmigielski et al., 2007b). Both compounds were identified with authentic standards using GC/EI-MS with prior derivatization, and in the case of 3-methyl-1,2,3-butanetricarboxylic acid also with LC/(-)ESI-MS. Their formation can be explained by further reaction of *cis*-pinonic acid, involving participation of the OH radical and $NO_x$. The detection of these compounds in field samples (Table 5) at high atmospheric concentrations illustrates that complex multi-generation chemistry characterizes the photooxidation of α/-β-pinene in the ambient atmosphere. Future laboratory chamber studies over a relatively long time scale should provide more detailed insights into the evolution of α/-β-pinene SOA constituents.

#### Organosulfates and nitrooxy organosulfates from α/-β-pinene SOA

Organosulfates and nitrooxy organosulfates from α/-β-pinene SOA products have been recently reported by Liggio and Li (2006a, b), Surratt et al. (2007a, 2008) and Iinuma et al. (2007b, Table 5). As in the case of isoprene SOA, the organosulfates and nitrooxy organosulfates from α/-β-pinene SOA products have a mixed biogenic/anthropogenic origin and should be regarded as processed (or aged) products. Liggio and Li (2006a, b) noted in laboratory experiments that pinonaldehyde is rapidly taken up on acidic sulfate aerosols, resulting in the formation of organosulfates, i.e., pinonaldehyde with a rather high vapor pressure (Hallquist et al., 1997) will be effectively transported into the condensed phase. Major compounds detected in ambient $PM_{2.5}$ aerosol are nitrooxy organosulfates from α-pinene SOA with a dihydroxypinane skeleton and a MW of 295. Laboratory and field experiments suggest that night-time $NO_3$ chemistry is important for the formation of these compounds (Surratt et al., 2007a, 2008; Iinuma et al., 2007b; Gómez-González et al., 2008); however, it should be noted that these compounds have also been observed in photooxidation (i.e., OH-initiated) experiments conducted in the presence of $NO_x$ and highly acidified sulfate seed aerosol (Surratt et al., 2008). Other organosulfates of α/-β-pinene SOA products found in ambient $PM_{2.5}$ aerosol include: derivatives of 3-hydroxyglutaric acid (Gómez-González et al., 2008; Surratt et al., 2008), 2,10-dihydroxypinane (Iinuma et al., 2007b; Surratt et al., 2008) and hydroxypinonic acid (Surratt et al., 2008). The organosulfates from α/-β-pinene SOA products are amphiphilic (i.e., both hydrophilic and hydrophobic) and may play an important role in aerosol microphysics. Only semi-quantitative data have been obtained so far using a surrogate standard (camphorsulfonic acid) for the organosulfate derivatives of dihydroxypinanes (Iinuma et al., 2007b). An upper limit estimate of the contribution from organosulfates to the particulate OM could be derived from the analysis of aerosol samples for total sulfur and water-soluble sulfate. Water-soluble sulfate is commonly measured by ion
chromatography (IC) and organosulfates are not included in this type of measurement. Total sulfur can be measured by X-ray emission techniques, such as X-ray fluorescence (XRF) or particle-induced X-ray emission spectrometry (PIXE), and this measurement will include the sulfur from water-soluble sulfate and other inorganic sulfur species (e.g., sulfite), the insoluble sulfur which may be associated with primary biogenic particles (Graham et al., 2003), and also the sulfur of the organosulfates. Subtracting the IC sulfate-sulfur from the XRF or PIXE sulfur will thus provide an upper limit for the sulfur that may be associated with organosulfates (assuming that sulfite sulfur and primary biogenic sulfur are negligible). It should be realized that XRF, PIXE, and IC analyses have an associated uncertainty of the order of 5% or more, so that the uncertainty that is associated with the difference will be quite substantial. For the site of K-puszta, Hungary, and a 2003 summer period, which was very dry and warm (Ion et al., 2005), the calculations indicated that organosulfates could be responsible for 20% of the total PM₁₀ sulfur concentration and 30% of the PM₁₀ OM (Maenhaut et al., 2008; Surratt et al., 2008). Using a similar approach, it was estimated that organosulfates accounted for 6–14% of the total PM₂.₅ sulfur concentration at the same site during summer 2006 (Lukács et al., 2009).

### 5.1.3 SOA tracers for the photooxidation of BVOCs other than isoprene and α/-β-pinene

Efforts have also been undertaken during the past five years to establish suitable tracers for SOA from the photooxidation of BVOCs other than isoprene and α/-β-pinene, which have lower but still significant emission rates, such as *d*-limonene and *β*-caryophyllene. Several compounds with a high molar yield were characterized in *d*-limonene/NOₓ SOA by Jaoui et al. (2006), including maleic acid, 4-isopropenyl-1-methyl-1-hydroxy-2-oxocyclohexane, ketonorlimonic acid and ketolimonic acid. Based on analysis of PM₂.₅ aerosol collected at Research Triangle Park (North Carolina, USA) during summer 2003, two compounds, i.e., 3-carboxyheptanedioic acid (MW 204) and an unknown product with a MW of 190, which were not present in α/-β-pinene SOA, were proposed as specific tracers for *d*-limonene SOA. With regard to the sesquiterpene *β*-caryophyllene, *β*-caryophyllinic acid was tentatively identified as a tracer and detected at a level up to 7 ng m⁻³ in PM₂.₅ summer aerosol collected at Research Triangle Park, North Carolina, USA (Jaoui et al., 2007).

As in the case of isoprene and α/-β-pinene SOA products, organosulfates have also been characterized for *d*-limonene SOA generated in the presence of highly acidic seed aerosol (Linuma et al., 2007a); more specifically, a sulfate adduct of endolim oxide (limonaldehyde; MW 282) was identified in ozonolysis SOA, as well as higher-MW oligomeric products (MWs 482 and 466) consisting of monomeric residues connected through a sulfate bridge. A recent study by Surratt et al. (2008) found that organosulfates/nitrooxy

organosulfates of all monoterpenes studied, which included *d*-limonene, *l*-limonene, α-terpinene, γ-terpinene, terpinolene, β-phellandrene, and Δ³-carene, also form in the presence of highly acidified sulfate seed aerosol when photooxidized in the absence/presence of NOₓ; however, unlike the organosulfates/nitrooxy organosulfates of α/-β-pinene, the organosulfates/nitrooxy organosulfates of these monoterpenes have so far not been detected in ambient aerosol. A selected number of NO₃-initiated oxidation experiments for α-pinene, *d*-limonene, and *l*-limonene were also conducted in the presence of highly acidified sulfate seed aerosol, resulting in the detection of organosulfates/nitrooxy organosulfates of these BVOCs. Finally, very polar organosulfates derived from oxidation products of unsaturated fatty acids and plant leaf volatiles were tentatively identified by Gómez-González et al. (2008) in PM₂.₅ summer aerosol collected from K-puszta, Hungary.

## 5.2 Condensed phase reactions and oligomerization

The role of condensed phase reactions in the formation and growth of SOA has been the subject of considerable interest over the last 5–10 years. Reactions of atmospheric oxidation products that increase the carbon number (accretion reactions) also cause the vapor pressure to be lowered by several orders of magnitude. This can have consequences for the ability of VOC oxidation products to form new particles (homogeneous nucleation), affect the growth rate of aerosol particles and also influence the total amount of SOA formed by incorporation of volatile oxidation products into the particle phase. An additional motivation to search for supplementary SOA formation pathways is the serious underestimation of SOA production in current atmospheric modeling approaches (Volkamer et al., 2006; Andreani-Aksoyoglu et al., 2008), as discussed in Sect. 4.3.2. Furthermore, because chemical reactions are taking place in the particle phase, the chemical composition of the existing aerosols, e.g., their acidity or ionic strength, could potentially influence SOA formation, representing a chemical coupling between previously unrelated parameters. There is additional interest in condensed phase reactions because they might help to explain the formation of other higher-MW products detected in atmospheric aerosol, such as HULIS (Gelencsér et al., 2002; Limbeck et al., 2003). HULIS are often detected in ambient atmospheric aerosols, cloud and fog water and show characteristics similar to those of humic and fulvic acids, especially their ultraviolet (UV) and fluorescence spectra (Havers et al., 1998a, b; Zappoli et al., 1999). However, the origin of this class of organic molecules is still unclear, as outlined in Sect. 3.3.3.

Numerous laboratory studies have shown evidence for the reactive uptake of volatile organic species during SOA formation as well as the formation of higher-MW organics in SOA. The first studies were based on indirect evidence, i.e., increased SOA mass concentration observed in the presence
of acidic seed aerosol (Tobias et al., 2000; Jang et al., 2002, 2004; Limbeck et al., 2003). Although it was proposed that polymerization reactions of volatile carbonyls may account for the observed SOA mass increase, there was no direct evidence to support this at the time. Subsequent studies utilized mass spectrometry for the direct detection of higher-MW products, and showed that oligomers, characterized by a highly regular mass difference pattern of 12, 14, 16 or 18 Da, constituted a considerable portion of the SOA (Kalberer et al., 2004; Tolocka et al., 2004; Gao et al., 2004a, b). It should be noted that the terms *oligomer* and *oligomerization*, as opposed to *polymer* and *polymerization*, are normally used in this context because, in the reaction systems studied to date, the MW range of the products is limited (IUPAC, 1996). As outlined in Sect. 3.3.3, the MS techniques which have been successfully applied for oligomer detection are mostly based on soft ionization techniques; either off-line techniques after a filter sampling/extraction step (Gao et al., 2004a, b; Iinuma et al., 2004; Surratt et al., 2006) or by on-line MS (Gross et al., 2006; Hearn and Smith, 2006; Surratt et al., 2006; Denkenberger et al., 2007; Heaton et al., 2007). Oligomerization processes of SOA components have even been observed using microscopic techniques (McIntire et al., 2005). Oligomers have been identified in SOA produced from the atmospheric oxidation of cyclic alkenes including terpenes (Gao et al., 2004a, b; Iinuma et al., 2004, 2007a; Tolocka et al., 2004; Baltensperger et al., 2005; Heaton et al., 2007; Müller et al., 2008), small open-chain alkenes including isoprene (Dommen et al., 2006; Sadezky et al., 2006; Surratt et al., 2006; Szmigielski et al., 2007a) and aromatic VOC precursors (Kalberer et al., 2004; Sato et al., 2007). In addition to these classical SOA precursor systems, other compounds which are present in atmospheric aerosols have also been investigated as possible contributors to oligomers, such as levoglucosan (Holmes and Petrucci, 2006) and oleic acid, although the motivation for investigating the chemical processing of oleic acid is often the search for an appropriate model system for heterogeneous reactions (Hearn et al., 2005; Hung et al., 2005; Mochida et al., 2006; Reynolds et al., 2006; Zahardis et al., 2006; Zahardis and Petrucci, 2007).

## 5.2.1 Molecular weight and analytical restrictions

One of the most important characteristics of oligomers in SOA is the MW range, since it not only affects vapor pressure and solubility of the compounds, but is also part of the definition of “higher-MW compounds”. Even for this basic feature the different investigations performed to date have identified species with a wide range of values, Table 6. However, it is likely that the wide MW range is a result of different operating conditions (e.g., oxidant, NOₓ regime, aerosol loading, and seed aerosol conditions) employed in the various studies.

Determination of the MW range of oligomers in SOA can be problematic. Several MS techniques (such as AMS based on EI or LDI) are not able to ionize larger organic molecules

<table><caption>**Table 6.** Molecular weight range of oligomers detected in laboratory studies of biogenic (BSOA) and anthropogenic SOA (ASOA) formation.</caption><thead><tr><th>Precursor system</th><th>Molecular weight range</th><th>Reference</th></tr></thead><tbody><tr><td>BSOA (α-pinene/O<sub>3</sub>)</td><td>200–900</td><td>Tolocka et al. (2004)</td></tr><tr><td>BSOA (α-pinene/O<sub>3</sub>)</td><td>250–1600</td><td>Gao et al. (2004a, b)</td></tr><tr><td>BSOA (α-pinene/O<sub>3</sub>)</td><td>up to 800</td><td>Iinuma et al. (2004, 2005)</td></tr><tr><td>BSOA (α-pinene/O<sub>3</sub>)</td><td>300–400</td><td>Müller et al. (2008)</td></tr><tr><td>BSOA (α-pinene/OH/hv, isoprene/OH/hv)</td><td>up to 600–700</td><td>Kalberer et al. (2006)</td></tr><tr><td>BSOA (isoprene/OH/hv)</td><td>up to 500–600</td><td>Dommen et al. (2006)<br>Surratt et al. (2006)<br>Szmigielski et al. (2007a)</td></tr><tr><td>BSOA (limonene/O<sub>3</sub>)</td><td>300–900</td><td>Iinuma et al. (2007a)</td></tr><tr><td>BSOA (β-pinene/O<sub>3</sub>)</td><td>up to 600</td><td>Iinuma et al. (2007b)</td></tr><tr><td>BSOA (terpenes/O<sub>3</sub>)</td><td>300–440</td><td>Heaton et al. (2007)</td></tr><tr><td>BSOA (Z-3-hexenyl acetate/O<sub>3</sub>)</td><td>up to 500</td><td>Hamilton et al. (2008)</td></tr><tr><td>ASOA (C<sub>5</sub>-C<sub>8</sub> cycloalkenes)</td><td>>250</td><td>Gao et al. (2004a)</td></tr><tr><td>ASOA (1,3,5-trimethylbenzene/OH/hv)</td><td>up to 1000</td><td>Baltensperger et al. (2005),<br>Kalberer et al. (2004)</td></tr><tr><td>ASOA (cyclohexene/O<sub>3</sub>)</td><td>>200</td><td>Sato (2005)</td></tr><tr><td>ASOA (cyclohexene, methylenecyclohexane/O<sub>3</sub>)</td><td>up to 300</td><td>Müller et al. (2007)</td></tr><tr><td>ASOA (toluene/OH/hv)</td><td>200–500</td><td>Sato et al. (2007)</td></tr></tbody></table>

without fragmentation, and thus have a tendency to underestimate MW. In contrast, the application of soft ionization techniques (e.g., ESI-MS) can lead to artificially high MWs due to ion-molecule reactions in the ion source of the mass spectrometer. Furthermore, thermal treatment of the organic aerosol prior to ionization and analysis, which is a common step in on-line aerosol characterization (e.g., thermodesnuder systems), might result in artificial oligomer formation (or decomposition) by influencing the equilibrium of condensation reactions. In general, the MW regimes reported more recently tend to be lower than the first estimates and are now mostly below 500. When chromatographic separations are performed before MS detection, ion source artifacts are excluded and the majority of oligomers possess MWs mostly below 400 (Hamilton et al., 2006; Surratt et al., 2006; Müller et al., 2007; Szmigielski et al., 2007a; Reinnig et al., 2008), which indicates the predominance of dimers and perhaps some trimers as oligomeric components in BSOA. However, it should be noted that chromatographic separation, ionization and detection all become more difficult as the size and polarity of the products increases and it is possible that higher oligomers may remain undetected. One example of the analytical difficulties encountered in recent studies concerns the oligomerization behavior of glyoxal. Glyoxal is formed during the photooxidation of biogenic and anthropogenic VOCs, is soluble in water (in form of the dihydrate) and can undergo hemiacetal/acetal formation. Therefore, several theoretical and experimental studies have been performed to evaluate the contribution of the C₂-dialdehyde to
SOA (Hastings et al., 2005; Kroll et al., 2005a; Liggio et al., 2005a, b; Loeffler et al., 2006; Offenberg et al., 2006; Carlton et al., 2007; Hu et al., 2007; Kua et al., 2008; Galloway et al., 2009; Nozière et al., 2009; Shapiro et al., 2009). Although all of these studies support the hypothesis that glyoxal oligomerization contributes to SOA, the MW (or MW distribution) of glyoxal oligomers has yet to be determined. The difficulties lie in the reversibility of the oligomerization reactions, i.e., when chromatographic separation techniques are applied the oligomers can hydrolyze and only the most stable oligomers will survive the separation and can be detected.

## 5.2.2 Oxygen content

Another characteristic of SOA oligomers is their very high oxygen content, at least when they are formed from biogenic precursors. HRMS delivers detailed information about the elemental composition of the oligomers and the results of several investigations published recently are shown in Table 7. All studies consistently report oxygen-to-carbon (O/C) ratios of about 0.4 to 0.5, showing that, on average, every second carbon atom is bonded to an oxygen atom. The exception is cyclohexene, which has the same functionality as the terpenes listed in Table 7 (i.e., one double bond) but with a smaller number of carbon atoms, where the observed O/C ratio of the oligomers can even reach 0.8. Interestingly, where data are available, the monomers appear to exhibit a higher maximum O/C ratio than observed in the oligomers, suggesting that condensation reactions might be involved in the oligomer formation process (Reinhardt et al., 2007; Walser et al., 2008). The measurement techniques used (HRMS) provide very reliable information on the elemental composition of the oligomers and potential chemical reaction pathways have to be able to account for the very high oxygen content of the products. It should also be noted that these experiments are mostly done at high precursor concentrations. As the O/C ratio tends to increase with decreasing aerosol load (Duplissy et al., 2008; Shilling et al., 2009) the O/C ratio may even be higher under atmospheric conditions.

## 5.2.3 Chemical nature and formation pathways

Although knowledge of the exact elemental composition of oligomers is important, it does not reveal their chemical nature. As a result, the formation pathways of oligomers in SOA remain unclear. SOA precursors typically produce a large range of oxidation products, resulting in a considerable number of possible reaction pathways. Table 8 lists some of the suggested high molecular weight products and their proposed formation pathways identified in SOA from alkene and terpene precursors.

The first six of the suggested products all contain a peroxide group, often as the linking functionality between the monomeric units (except the hydroperoxides). In general, the proposed peroxide structures contain a large number of

<table><caption>**Table 7.** Measured O/C ratio of BSOA oligomers.</caption><thead><tr><td>Precursor</td><td>O/C ratio</td><td>Reference</td></tr></thead><tbody><tr><td>α-pinene</td><td>0.4–0.57</td><td>Reinhardt et al. (2007)</td></tr><tr><td>α-pinene</td><td>0.3–0.5</td><td>Müller et al. (2008)</td></tr><tr><td>Limonene</td><td>0.43–0.5</td><td>Walser et al. (2008)<sup>a</sup></td></tr><tr><td>Limonene</td><td>0.45</td><td>Heaton et al. (2007)<sup>a</sup></td></tr><tr><td>β-pinene</td><td>0.45</td><td>Heaton et al. (2007)<sup>a</sup></td></tr><tr><td>Δ<sup>3</sup>-carene</td><td>0.43</td><td>Heaton et al. (2007)<sup>a</sup></td></tr><tr><td>Sabinene</td><td>0.37</td><td>Heaton et al. (2007)<sup>a</sup></td></tr><tr><td>Cyclohexene</td><td>0.42–0.8</td><td>Müller et al. (2008)</td></tr><tr><td>Cyclohexene</td><td>0.54–0.8</td><td>Hamilton et al. (2006)</td></tr></tbody></table>

<sup>a</sup> Overall O/C ratio for monomers and oligomers.

oxygen atoms, which is in line with the high O/C ratio measurements discussed above. The next subgroup are the non-covalently bonded dimers of organic acids, which are likely to exist in the particle phase and may also be involved in the early steps of new particle formation processes. However, since SOA oligomers have been shown to survive chromatographic separation, which is very unlikely for non-covalently bonded oligomers, other covalently bonded candidates must exist (Müller et al., 2009). Another subgroup of oligomeric products contains esters and anhydrides, both derivatives of carboxylic acids, which are known to be formed in large amounts in laboratory studies of SOA formation. In principle, classical esterification reactions between an alcohol moiety and a carboxylic acid group could be the pathway for ester formation, as recently proposed for oligoesters formed in isoprene high-NOₓ SOA (Surratt et al., 2006; Szmigielski et al., 2007a). However, other precursor molecules for ester formation are also possible (epoxides, anhydrides). Finally the last two suggested product groups are connected to carbonyl chemistry (especially aldehyde chemistry); the formation of hemiacetals/acetals, which is often connected with initial hydrolysis of the carbonyl to form the carbonyl hydrate, and the aldol reaction, the only pathway to form carbon-carbon bonds between the monomeric building blocks.

Figure 7 schematically shows the different chemical pathways that have been proposed to yield covalently bonded oligomers and other higher-MW products. The upper part of the figure contains oligomerization reactions that are believed to take place in the gas phase, i.e., reaction pathways involving radicals. Although some of these reactions (e.g., peroxy radical recombinations) could also happen in the ambient atmosphere (Madronich and Calvert, 1990), the reactions involving the stabilized Criegee intermediate (SCI) as reactant are unlikely to take place under atmospheric conditions, since reaction with water molecules is considered the most important reaction pathway for these species. However, in laboratory experiments performed under low humidity conditions these pathways might have contributed to the oligomers observed in simulation chambers (Sadezky et al., 2006). Furthermore, the formation pathways yielding
<table><caption>**Table 8.** Suggested products and formation pathways for oligomers and other higher-MW products in SOA.</caption><thead><tr><th>Suggested products</th><th>Suggested formation pathway</th><th>References</th></tr></thead><tbody><tr><td>Oligoperoxides</td><td>Stabilized Criegee Intermediate (SCI) self-reaction</td><td>Sadezky et al. (2006)</td></tr><tr><td>Peroxyhemiacetals</td><td>Hydroperoxides + carbonyls (aldehydes)</td><td>Docherty et al. (2005), Tobias and Ziemann (2000, 2001), Tobias et al. (2000), Surratt et al. (2006)</td></tr><tr><td>Alkoxyhydroperoxides</td><td>SCI + alcohols</td><td>Tobias and Ziemann (2000), Ziemann (2003)</td></tr><tr><td>Acyloxyhydroperoxides</td><td>SCI + carboxylic acids</td><td>Tobias and Ziemann (2001), Tobias et al. (2000)</td></tr><tr><td>Secondary ozonides</td><td>SCI + carbonyls (aldehydes)</td><td>Heaton et al. (2007), Tobias and Ziemann (2001), Tobias et al. (2000), Walser et al. (2008)</td></tr><tr><td>ROOR-type peroxides</td><td>Peroxyradical self-reaction</td><td>Ng et al. (2008), Ziemann (2002)</td></tr><tr><td>Dicarboxylic acid dimers</td><td>Formation of non-covalent diacid adducts</td><td>Hoffmann et al. (1998), Jenkin (2004), Sloth et al. (2004)</td></tr><tr><td>Esters</td><td>Unknown (alcohol + carboxylic acid, anhydride + alcohol, epoxide + carboxylic acid)?</td><td>Hamilton et al. (2006, 2008), Müller et al. (2007, 2008), Reinnig et al. (2008), Surratt et al. (2006), Szmigielski et al. (2007a)</td></tr><tr><td>Anhydrides</td><td>Unknown</td><td>Hamilton et al. (2006)</td></tr><tr><td>Hemiacetals/acetals</td><td>Carbonyls + (alcohol)</td><td>Liggio and Li (2006a, b), Surratt et al. (2006), Tolocka et al. (2004b), Iinuma et al. (2004)</td></tr><tr><td>Aldol condensation products</td><td>Carbonyls</td><td>Barsanti and Pankow (2005), Czoschke and Jang (2006b), Garland et al. (2006), Liggio and Li (2006a, b), Nozière et al. (2007), Tolocka et al. (2004b)</td></tr></tbody></table>

higher-MW products in the gas phase might very well be involved in new particle formation processes such as nucleation, especially under laboratory conditions.

The lower right part of Fig. 7 contains reactions that are believed to take place within the condensed phase. Here, gas-particle transfer precedes the oligomer forming reaction and, if the partitioning coefficient indicates that the monomer will exist mainly in the gas phase, the oligomerization process can be treated as reactive uptake. As indicated in Fig. 7, these reactions are generally acid-catalyzed, suggesting that a form of chemical coupling exists between aerosol composition and the formation of SOA. Within this context, the most widely investigated processes involve the acid catalyzed reactions of aldehydes or ketones (aldol reaction/condensation, acetal formation) on the surface and in the bulk of particles, see

Sect. 5.3. However, oligomers were observed also in the absence of acidic seed aerosols (Kalberer et al., 2004; Baltensperger et al., 2005; Dommen et al., 2006; Surratt et al., 2006; Szmigielski et al., 2007a). In addition, the water content (water activity) of aerosols will also influence the uptake of organic compounds. Therefore, a straightforward interpretation of the acidity influence is difficult to achieve. Similar issues are found in the interpretation of data obtained from field studies using on-line MS techniques (Denkenberger et al., 2007; Zhang et al., 2007a). Nevertheless, as an initial summation of the work that has been performed to date, one might conclude that particles with higher acidity are likely to promote the formation of larger oligomers. However, the relative importance of these oligomerization processes under ambient atmospheric conditions remains to be quantified.