Why do these two approaches that more or less equally capture the chamber-generated SOA properties exhibit such different oxidation trajectories in OS C -n C space? While FGOM exhibits the pull of oligomers, the oxidation trajectory generated by SOM progressively evolves in a direction that involves significant formation of highly oxidized small fragments. In the SOM, the individual species are characterized by the average carbon and oxygen number (n O /n C pairs), pairs that do not necessarily correspond to actual compounds, since functional group information is not explicitly simulated. Again, for the dodecane low-NO x case, the ∆LVP and [1O, 2O, 3O, 4O] values are 2.2 and [0.79, 0.17, 0.03, 0.0], respectively, meaning that upon one generation of oxidation, the average number of oxygens added is 1.22 and the total decrease of vapor pressure on a log scale is 2.7, which equals the addition of approximately 1.3 –OH, 1.2 –OOH, 1.2 – ONO 2 or 2.4 –C=O groups. This intense decrease of volatility upon only one generation allows the formation of small highly oxygenated fragments but with sufficiently low volatilities to contribute appreciably to the particle phase. As noted, these small fragments, as represented by n O /n C pairs, are not actual products reflected in current gas- phase photochemical mechanisms. However, their presence in the particle phase in a significant amount shifts the oxidation trajectory to the upper right corner in OS C -n C
344 A. R. METCALF ET AL.
followed by one in which growth is quenched while the cham- ber undergoes dilution. The dilution phase is used as a means to assess SOA volatility by measuring the possible evaporation of coatings on the rBC seed. In the experiments presented here, a 3-λ photoacoustic soot spectrometer is used to measure the optical properties of the uncoated rBC seed, initially, and the coated rBC seed during the course of SOA formation. These measurements, coupled with the application of a core-and-shell Mie scattering model, allow one to infer the optical properties of the SOA. Application of a prototype single-particle angularly- resolved light scattering instrument confirms that the uncoated rBC particles are nonspherical. Important to understanding the effect rBC has on SOA formation is whether or not SOA con- densed onto rBC seed is chemically and optically similar to nu- cleated SOA under dry conditions. High-resolution Aerodyne aerosol mass spectrometer measurements for the three systems considered here, naphthalene photooxidation and photooxida- tion of α-pinene under both high- and low-NO x conditions, confirm that the composition of SOA coating rBC seed particles differs from homogeneously nucleated SOA by no more than condensing SOA on the more conventional ammonium sulfate seed used in many chamber experiments, so that the use of rBC as a seed is not expected to alter the basic chemistry of SOA formation under dry conditions. Both SP2 and PASS-3 measure- ments reveal a change in the SOA coating and particle optical properties during SOA growth in the high-NO x α-pinene sys- tem, which is mirrored by a corresponding change in the AMS mass spectra. The combination of SP2 and AMS measurements in this system suggest that semivolatile species are evaporating from the aerosol during chemical aging. A change in optical properties during SOA growth in the low-NO x α -pinene system is mirrored by a change in organic growth rate and AMS mass spectra, but not in single-particle coating thicknesses. Explo- ration of a fundamental explanation of the chemistry leading to these changes lies beyond the scope of the present work. We have provided a framework by which future studies of SOA optical properties and single-particle growth dynamics may be explored in environmental chambers.
Chapter 4 - SOA Temporal Evolution
135 For the identified species, (4-methoxyphenyl)acetic acid, photolysis degradation is considered to be negligible (Jenkin et al., 1997). However, the identification of an oxidation product, with an additional • OH group on the aromatic ring (3-hydroxy-4- methoxyphenyl)acetic acid (Chapter 3, Section 3.3.6, Table 3.5, compound 4) indicates further gas phase and/or heterogeneous reactions are occurring. Routes (i) and (ii) will result in a decrease in the compounds gas phase concentration, which could result in net re- evaporation into the gas phase. Recently, Vanden et al. (2010) found ambient SOA and laboratory generated α-pinene SOA did not follow the reversible gas-particle partitioning equilibrium assumed for liquid state particles (Vaden et al., 2011). The removal of gas phase organics did not result in the complete re-evaporation the SOA species into the gas phase, indicating the SOA studied was not liquid-like, and that re-evaporation was slow and kinetically limited (Vaden et al., 2011). For an experimental time of ~ 4 hours, approximately 45% of the SOA remained in the aerosol phase, whether the SOA was liquid or solid state, aged or not (Vaden et al., 2011). Considering this, gas phase loss processes are unlikely to solely account for the removal of a species from the aerosol phase. It is therefore likely that these species are also being lost as a result of heterogeneous or in- particle phase reactions, changing their chemical composition and resulting in the compound not being observed at the end of the experiment.
Laboratorychambers, invaluable in atmospheric chemistry and aerosolformation studies, are subject to particle and vapor wall deposition, processes that need to be accounted for in order to accurately determine secondaryorganicaerosol (SOA) mass yields. Although particle wall deposition is rea- sonably well understood and usually accounted for, vapor wall deposition is less so. The effects of vapor wall deposition on SOA mass yields in chamber experiments can be constrained exper- imentally by increasing the seed aerosol surface area to promote the preferential condensation of SOA-forming vapors onto seed aerosol. Here, we study the influence of seed aerosol surface area and oxidation rate on SOA formation in α-pinene ozonolysis. The observations are analyzed using a coupled vapor-particle dynamics model to interpret the roles of gas-particle partitioning (quasi- equilibrium vs. kinetically-limited SOA growth) and α-pinene oxidation rate in influencing vapor wall deposition. We find that the SOA growth rate and mass yields are independent of seed surface area within the range of seed surface area concentrations used in this study. This behavior arises when the condensation of SOA-forming vapors is dominated by quasi-equilibrium growth. Faster α-pinene oxidation rates and higher SOA mass yields are observed at increasing O 3 concentrations for the same initial α-pinene concentration. When the α-pinene oxidation rate increases relative to vapor wall deposition, rapidly produced SOA-forming oxidation products condense more readily onto seed aerosol particles, resulting in higher SOA mass yields. Our results indicate that the extent to which vapor wall deposition affects SOA mass yields depends on the particular VOC system, and can be mitigated through the use of excess oxidant concentrations.
Joshi et al., 1982) and wall interactions (McMurry and Rader, 1985; McMurry and Grosjean, 1985; Pierce et al., 2008).
On the other hand, smog chambers with lower oxidant con- centrations and longer residence times may more closely simulate atmospheric oxidation. All laboratory reactors are imperfect simulations of the atmosphere because they have walls that cause particle loss and can influence the chemistry of semivolatile organics and, thus, particle growth and com- position (Matsunaga and Ziemann, 2010). Therefore, utiliz- ing flow tubes and smog chamber reactors with different de- signs can complement each other, making it possible to ex- tend studies over a range of parameters unattainable by either method individually, and ultimately lead towards a better un- derstanding of atmospheric aerosolprocesses. The results of laboratoryaerosol experiments are used as inputs to climate models. Therefore, the evaluation of experimental uncertain- ties associated with measurements is needed for reliable ap- plication. The characterization of different reactor designs is important to establish the reliability of the experimental techniques.
The volume of the reactor limits the duration of experiments in a chamber operated in batch mode; when sampling with all instruments, nearly half of the chamber volume is depleted in 18 h, at which point it is preferable to cease sampling due to deflation of the chamber. To achieve longer OH expo- sure times with all instruments sampling, sets of experiments were conducted with increasing duration and staggered in- strument sampling. Instruments were grouped into two cate- gories based upon their sampling schedule. Group I includes the AMS and a RH and temperature probe. Group II includes the DMA, the CIMS, the GC/FID, the O 3 analyzer, and the NO x analyzer. All instruments were operated during initial injections before the onset of irradiation. Experimental time began at the onset of irradiation. First, two 18 h experiments were conducted with Group I and II instruments sampling for the entire duration to establish consistency in the gas and particle phases during separate experiments. Subsequent ex- periments of 24 h, 30 h, and 36 h were conducted to achieve longer OH exposure. The instrument sampling schedule for all experiments is given in Table 1. The entire set of 5 ex- periments was conducted in the same chamber to avoid any differences between chamber conditions that may arise be- tween the dual chambers. For each of the Group II instru- ments, the data from all experiments were combined to track the evolution of species for the entire 36 h of OH exposure.
Aerosols are suspended solid or liquid particles and they affect air quality, human health and the earth’s climate. Aerosols can be classified into two main categories according to their formationprocesses. Primary aerosols are emitted directly from different sources into the atmosphere, while the oxidation of organic gases leads to the formation of low-volatility products that partition into the condensed phase and result in the formation of secondaryorganicaerosol (SOA). Biogenic hydrocarbons emitted by vegetation and aromatics from anthropogenic sources are important precursors for SOA formation. The main oxidants in the atmosphere are ozone (O 3 ), hydroxyl radical (OH), and nitrate radical (NO 3 ). SOA contributes significantly to the total ambient organic aerosols in urban areas, as well as regionally and globally.
a small amount of time of IEPOX processed in cloudwater can lead to considerable SOA growth.
Figure 4. Comparison of IEPOX SOA formation rates in Simulation 1 for aerosol pH 1 – 4 and cloud pH 3 – 6. Rates are averaged over the entire six hours of the corresponding phase. Each individual
point corresponds to the IEPOX SOA formation rate in cloudwater in the second half of the simulation with the pH of the aerosol in the first half of the simulation represented by the shape and color of the point as described in the legend. The same colors are used in the horizontal dashed
I would also like to thank my two biggest mentors in lab, Jesse Kroll and Sally Ng, from whom I learned everything I needed to know about anything, ranging from RO 2 chemistry and aerosol science to Swagelok fittings and chemical syntheses. I had the great opportunity to work with Jesse Kroll on my first project, and overlapped with him at Caltech for a few months. He has introduced to me a dynamic framework to think about organic aerosols. From working with him closely on one of his papers and one of my own, I have learned how to write an interesting paper and to present it coherently and logically. His interest in online videos almost parallels his interest in science, both of which made the roof lab a source of great science and great fun. Among all the people whom I have worked with, Sally is the one who has taught me the most. She has shown me how to run the roof lab chambers, and that is the least important lesson I have learned from her. Her relentless scientific pursuit (always insisting every piece of instrument be run to get the most data) and borderline superstitious attitude have been the most impressive to me. She has also been the most passionate about science, and would always come to lab in the morning with new ideas and questions she thought of overnight. Always cheerful, never down, she has the most positive attitude about everything and everyone, which is infectious upon people around her. She has taught me the importance of returning borrowed items just as they were before they were borrowed, singing during chemical syntheses, and asking for help when it is needed. Together, Jesse and Sally have done some great work during the years they were here, but more importantly, they have made the roof lab the best community one can work in.
p o st evaporation o f the hydrom eteor (B lando and Turpin, 2000; K anakidou e t al., 2005). R ecent experim ental and m odeling studies indeed dem onstrated that SO A potentially can be form ed from aqueous-phase processing o f organic com pounds in clouds (C arlton et al., 2006; Loeffler et al., 2006). A m bient particle size distribution m easurem ents also show ed the occurrence o f droplet-m ode organics (B lando et al., 1998; Y ao et al., 2002), w hich, sim ilarly to droplet-m ode sulfate, are m ost likely form ed from cloud processing o f organic m aterials (B lando and Turpin, 2000). Several m odeling studies (W am eck, 2003; E rvens et al., 2004; Lim et al., 2005) have dem onstrated the form ation o f low -m olecular w eight dicarboxylic acid from cloud processing o f organics. Such dicarboxylic acids have been found in atm ospheric aerosols in various regions (K aw am ura and Ikushim a, 1993; D ecesari et al., 2000). In addition, Claeys et al. (2004) show ed that m ultiphase acid-catalyzed organic reactions with hydrogen peroxide provided a new route for SO A form ation from isoprene and hypothesized that such a m echanism could also provide a pathw ay for SO A form ation from m onoterpenes and their oxidation products. M ost recently, H eald et al. (2006) analyzed the covariance o f w ater soluble particulate organics w ith other species in the free troposphere over the eastern U nited States, w ith the results suggesting aqueous-phase SO A generation involving biogenic precursors.
case, corresponding to approximately 10 to 20%, respectively, of the total SOA mass formed.
Identification of IEPOX as the Intermediate Responsible for Acid- Enhanced Isoprene SOA. We hypothesize that particle-phase reac- tions of IEPOX play a significant role in the formation of the other major low-NO x SOA constituents shown in Fig. 1H–L, as well as in the enhancement of total SOA mass. To test this hypothesis, we synthesized 2,3-epoxy-1,4-butanediol (BEPOX) (see Materials and Methods), which is the butadiene derivative of IEPOX, and conducted reactive uptake experiments in the pre- sence of both neutral and highly acidified sulfate seed aerosol. BEPOX is used in these experiments instead of IEPOX because precursors for IEPOX are not commercially available. In these dark and dry (<10% RH) experiments, no OH precursor (e.g., H 2 O 2 ) or NO x was present; thus, only reactive uptake of BEPOX onto seed aerosol occurred. Two variations of these reactive up- take experiments were carried out: (i) BEPOX was added first, followed by the injection of seed aerosol; or (ii) seed aerosol was added first, followed by the injection of BEPOX. CIMS time traces corresponding to version (i) of the BEPOX reactive uptake experiments are shown in Fig. 2A. The only parameter varied was the acidity of the sulfate seed aerosol. BEPOX is rapidly removed from the gas phase within the first hour after the acidified sulfate seed aerosol is injected into the well-mixed chamber. Upon the injection of neutral sulfate seed aerosol, BEPOX disappears from
* now at: NILU, Norwegian Institute for Air Research, Kjeller, Norway
Received: 1 December 2008 – Published in Atmos. Chem. Phys. Discuss.: 29 January 2009 Revised: 11 August 2009 – Accepted: 27 August 2009 – Published: 22 September 2009
Abstract. The role of isoprene as a precursor to secondaryorganicaerosol (SOA) over Europe is studied with the two- way nested global chemistry transport model TM5. The in- clusion of the formation of SOA from isoprene oxidation in our model almost doubles the atmospheric burden of SOA over Europe compared to SOA formation from terpenes and aromatics. The reference simulation, which considers SOA formation from isoprene, terpenes and aromatics, predicts a yearly European production rate of 1.0 Tg SOA yr − 1 and an annual averaged atmospheric burden of about 50 Gg SOA over Europe. A fraction of 35% of the SOA produced in the boundary layer over Europe is transported to higher altitudes or to other world regions. Summertime measurements of or- ganic matter (OM) during the extensive EMEP OC/EC cam- paign 2002/2003 are better reproduced when SOA formation from isoprene is taken into account, reflecting also the strong seasonality of isoprene and other biogenic volatile organic compounds (BVOC) emissions from vegetation. However, during winter, our model strongly underestimates OM, likely caused by missing wood burning in the emission inventories. Uncertainties in the parameterisation of isoprene SOA for- mation have been investigated. Maximum SOA production is found for irreversible sticking (non-equilibrium partitioning) of condensable vapours on particles, with tropospheric SOA production over Europe increased by a factor of 4 in sum- mer compared to the reference case. Completely neglecting SOA formation from isoprene results in the lowest estimate (0.51 Tg SOA yr − 1 ). The amount and the nature of the ab-
Predicting secondaryorganicaerosolformation from terpenoid ozonolysis with varying yields in indoor environments
The ozonolysis of terpenoids generates secondaryorganicaerosol (SOA) indoors. Models of var- ying complexity have been used to predict indoor SOA formation, and many models use the SOA yield, which is the ratio of the mass of produced SOA and the mass of consumed reactive organic gas. For indoor simulations, the SOA yield has been assumed as a constant, even though it de- pends on the concentration of organic particles in the air, including any formed SOA. We devel- oped two indoor SOA formation models for single terpenoid ozonolysis, with yields that vary with the organic particle concentration. The models have their own strengths and were in agree- ment with published experiments for d-limonene ozonolysis. Monte Carlo analyses were per- formed that simulated different residential and office environments to estimate ranges of SOA concentrations and yields for d- limonene and α-pinene ozonolysis occurring indoors. Results in- dicate that yields are highly variable indoors and are most influenced by background organic par- ticles for steady state formation and indoor ozone concentration for transient peak formation. Ad- ditionally, a review of ozonolysis yields for indoor-relevant terpenoids in the literature revealed much uncertainty in their values at low concentrations typical of indoors.
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; Kub´atov´a 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).
While we have demonstrated good agreement between simpleGAMMA and GAMMA, the limitations of GAMMA also apply to simpleGAMMA; for example, neither model includes a treatment of oxidative aging of aaSOA at this time due to a lack of kinetic and mechanistic data. As a result, overprediction of total aaSOA mass is likely (Budisulistior- ini et al., 2015). The only sources of aqueous-phase OH in GAMMA are HOOH photolysis or Henry’s law transfer of OH from the gas phase. Therefore, we, like others (Waxman et al., 2013; Ervens et al., 2014), have observed OH-limited chemistry in the aqueous aerosol phase using GAMMA, and this informed the simpleGAMMA formulation. While tran- sition metal ion chemistry, a possible source of OH (Her- rmann et al., 2015), was not included in the first version of GAMMA (McNeill et al., 2012) due to the focus on ammo- nium sulfate aerosols in that study, these mechanisms may be active in ambient aerosols. Preliminary calculations in GAMMA show that including transition metal ion (Fe +3 , Cu +2 , Mn +3 ) chemistry following CAPRAM 3.0 (Chemical Aqueous Phase Radical Mechanism; Herrmann et al., 2005) does not perturb the predicted aaSOA yield or product dis- tribution. Aqueous-phase diffusion is not accounted for in GAMMA or simpleGAMMA, that is, Henry’s law equili- bration is assumed to occur instantaneously and no spatial concentration gradients within the particle are considered. This likely leads to an overestimate of OH chemistry when this highly reactive species is taken up from the gas phase. However, since we have found that aqueous-phase photo- chemistry does not dominate aaSOA formation, inclusion of aqueous-phase diffusion limitations in this calculation would not change our results or the formulation of simpleGAMMA. Aqueous-phase diffusion may also be important for relatively large droplets such as those encountered in marine aerosols.
the real atmosphere.
In all low concentration experiments, due to the slower reaction, the ΔVOC is better constrained for longer periods, and the mixing time scale is faster relative to reactions, resulting in yield curves with less uncertainty but with the same inter- BVOC trend observed at both concentration conditions. Due to these di ﬀerences, subsequent discussion will focus only on these low concentration experiments. As is apparent in Figure 3, the high concentration experiments (and early stages of some low concentration experiments) produce unrealistically high yields, especially in the full kinetics model, suggesting that in these cases, reactions were faster than chamber mixing and that this box model may be inaccurate. According to absorptive partitioning theory, 31 −34 yields should increase with aerosol mass loading, assuming a constant product distribution over time. If, however, the product distribution is changing over time as RO 2 reactive fate changes, then mass yields could appear to decrease as an artifact of a shift from early high molecular weight products to later lower molecular weight products. As a result of these uncertainties at early times, we do not ﬁt the mass-dependent yield for these experiments, but rather report yields at 10 μg m −3 for each low concentration experiment (Table 3). We choose 10 μg m −3 because it is su ﬃciently late in the low concentration experiments (generally ∼1.5 h) that irregularities in the yield curve have subsided.
Experimental results have been combined with structure- activity relationships, and sometimes theoretical quantum- level calculations (Peeters et al., 2001), to construct detailed mechanisms for the gas phase oxidation of α-pinene, which have been supplemented with a partitioning model (Kamens and Jaoui, 2001; Jenkin, 2004; Capouet et al., 2008; Xia et al., 2008; Valorso et al., 2011). Such detailed mechanisms are often too large for use in global chemistry transport mod- els, however. Moreover, these models still contain many un- certainties (Hallquist et al., 2009), which can lead to discrep- ancies between modelled and experimental SOA yields (Xia et al., 2008; Ceulemans et al., 2010; Valorso et al., 2011). Another approach towards SOA modelling has been the di- rect fitting of parameterised two-product models for SOA formation to experimental SOA mass yields (Odum et al., 1996), and for α-pinene a number of parameterisations has been derived (see Sect. 3.5). Most smog chamber experi- ments were hitherto conducted under conditions which, for one or more aspects, differ from those which can be found in the atmosphere: initial VOC loadings are often higher, NO x