Secondaryorganicaerosol (SOA) constitutes a major fraction of sub-micrometer atmospheric particulate matter. Quantitative simulation of SOA within air quality and climate models—and its resulting impacts—depends on the translation of SOA formation observed in laboratory chambers into robust parameterizations. Worldwide data have been accumulating indicating that model predictions of SOA are substantially lower than ambient observations. While possible explanations for this mismatch have been advanced, none has addressed the laboratory chamber data themselves. Losses of particles to the walls of chambers are routinely accounted for, but there has been little evaluation of the effects on SOA formation of losses of semi-volatile vapors to chamber walls. Here, we experimentally demonstrate that such vapor losses can lead to substantially underestimated SOA formation, by factors as much as 4. Accounting for such losses has the clear potential to bring model predictions and observations of organicaerosol levels into much closer agreement.
Abstract. The secondaryorganicaerosol (SOA) module in the Model for Ozone and Related Chemical Tracers, version 4 (MOZART-4) was updated by replacing ex- isting two-product (2p) parameters with those obtained from two-product volatility basis set (2p-VBS) fits (MZ4- C1), and by treating SOA formation from the follow- ing additional volatile organic compounds (VOCs): iso- prene, propene and lumped alkenes (MZ4-C2). Strong sea- sonal and spatial variations in global SOA distributions were demonstrated, with significant differences in the pre- dicted concentrations between the base case and updated model simulations. Updates to the model resulted in sig- nificant increases in annual average SOA mass concen- trations, particularly for the MZ4-C2 simulation in which the additional SOA precursor VOCs were treated. An- nual average SOA concentrations predicted by the MZ4- C2 simulation were 1.00 ± 1.04 µg m −3 in South America, 1.57 ± 1.88 µg m −3 in Indonesia, 0.37 ± 0.27 µg m −3 in the USA, and 0.47 ± 0.29 µg m −3 in Europe with corresponding increases of 178, 406, 311 and 292 % over the base-case sim- ulation, respectively, primarily due to inclusion of isoprene. The increases in predicted SOA mass concentrations resulted in corresponding increases in SOA contributions to annual average total aerosol optical depth (AOD) by ∼ 1–6 %. Esti- mated global SOA production was 5.8, 6.6 and 19.1 Tg yr −1 with corresponding burdens of 0.22, 0.24 and 0.59 Tg for the base-case, MZ4-C1 and MZ4-C2 simulations, respectively. The predicted SOA budgets fell well within reported ranges for comparable modeling studies, 6.7 to 96 Tg yr −1 , but were lower than recently reported observationally constrained val- ues, 50 to 380 Tg yr −1 . For MZ4-C2, simulated SOA con- centrations at the surface also were in reasonable agreement
wood soot, and the gas-phase organic compounds can affect the gas-particle equilibrium partitioning (Leach et al. 1999). These studies have focused on the partitioning between gas and particle phases rather than any unique chemical or physical properties of either the gases or the soot. Studies at the Aerosols, Interactions and Dynamics in the Atmosphere (AIDA) chamber facility re- port the enhancement of light absorption by soot seed particles coated with α-pinene ozonolysis SOA (Saathoff et al. 2003) and evaluate absorption enhancements by particle coagulation and growth of SOA (Schnaiter et al. 2003, 2005). These studies have focused primarily on the optical and morphological prop- erties of the soot as growth of SOA or coagulation with other particles occurs in the chamber. Studies at the Carnegie Mellon University environmental chamber found that SOA formed by the photooxidation of evaporated diesel exhaust exceeded by a wide margin the SOA mass predicted by a model including the classified hydrocarbons (Weitkamp et al. 2007). A study at the Paul Scherrer Institute (PSI) smog chamber character- ized the chemical composition and emission factors of primary and secondaryorganicaerosol from three diesel vehicles with various engine after-treatment systems (Chirico et al. 2010). These studies have focused on characterizing SOA from the many gas-phase precursors found in diesel exhaust or on the efforts to clean up the exhaust from diesel engines. Most of the studies listed above used fresh diesel or wood soot, which typically has a thin layer of nonrefractory material on the rBC or has soot co-emitted with many gas-phase species, such as PAHs. Uncoated spark-generated soot was also used and has been found to be chemically and optically different than diesel soot and is, therefore, not a good surrogate for atmospheric rBC (Kirchner et al. 2003; Schnaiter et al. 2003). Commercially available fullerene soot is a fractal, hydrophobic particle that most resembles ambient rBC in the SP2 instrument (Moteki and Kondo 2010; Laborde et al. 2012) and is structurally similar to diesel soot (Moteki et al. 2009), although chemical and optical comparisons to ambient soot have yet to be reported.
Laboratory chambers provide a controlled environment to study the formation and evolution of secondaryorganicaerosol, by isolating specific compounds of interest and controlling the oxidation environment. Since identification and quantification of all oxidation products from parent hydrocarbons are difficult, aerosol yields have been used in the study of secondary precursor organics. Aerosol yields indicate the aerosol-forming potential of various precursor organics. Yield is defined as the ratio of the mass concentration of aerosol formed from the oxidation of a given parent hydrocarbon to that of the hydrocarbon reacted: Y = ΔM o / ΔHC, where ΔM o (μg m -3 ) is the organicaerosol
Abstract. We use a global aerosol microphysics model in combination with an offline radiative transfer model to quantify the radiative effect of biogenic secondaryorganicaerosol (SOA) in the present-day atmosphere. Through its role in particle growth and ageing, the presence of bio- genic SOA increases the global annual mean concentration of cloud condensation nuclei (CCN; at 0.2 % supersatura- tion) by 3.6–21.1 %, depending upon the yield of SOA pro- duction from biogenic volatile organic compounds (BVOCs), and the nature and treatment of concurrent primary carbona- ceous emissions. This increase in CCN causes a rise in global annual mean cloud droplet number concentration (CDNC) of 1.9–5.2 %, and a global mean first aerosol indirect effect (AIE) of between + 0.01 W m −2 and − 0.12 W m −2 . The ra- diative impact of biogenic SOA is far greater when biogenic oxidation products also contribute to the very early stages of new particle formation; using two organically mediated mechanisms for new particle formation, we simulate global annual mean first AIEs of − 0.22 W m −2 and − 0.77 W m −2 . The inclusion of biogenic SOA substantially improves the simulated seasonal cycle in the concentration of CCN-sized particles observed at three forested sites. The best correlation is found when the organically mediated nucleation mecha- nisms are applied, suggesting that the first AIE of biogenic SOA could be as large as − 0.77 W m −2 . The radiative im- pact of SOA is sensitive to the presence of anthropogenic emissions. Lower background aerosol concentrations simu-
Abstract. The global secondaryorganicaerosol (SOA) bud- get is highly uncertain, with global annual SOA produc- tion rates, estimated from global models, ranging over an order of magnitude and simulated SOA concentrations un- derestimated compared to observations. In this study, we use a global composition-climate model (UKCA) with interac- tive chemistry and aerosol microphysics to provide an in- depth analysis of the impact of each VOC source on the global SOA budget and its seasonality. We further quan- tify the role of each source on SOA spatial distributions, and evaluate simulated seasonal SOA concentrations against a comprehensive set of observations. The annual global SOA production rates from monoterpene, isoprene, biomass burning, and anthropogenic precursor sources is 19.9, 19.6, 9.5, and 24.6 Tg (SOA) a −1 , respectively. When all sources are included, the SOA production rate from all sources is 73.6 Tg (SOA)a −1 , which lies within the range of estimates from previous modelling studies. SOA production rates and SOA burdens from biogenic and biomass burning SOA sources peak during Northern Hemisphere (NH) summer. In contrast, the anthropogenic SOA production rate is fairly constant all year round. However, the global anthropogenic SOA burden does have a seasonal cycle which is lowest dur- ing NH summer, which is probably due to enhanced wet re- moval. Inclusion of the new SOA sources also accelerates the ageing by condensation of primary organicaerosol (POA), making it more hydrophilic, leading to a reduction in the POA lifetime. With monoterpene as the only source of SOA, simulated SOA and total organicaerosol (OA) concentrations are underestimated by the model when compared to surface
Abstract. Multi-generational gas-phase oxidation of organic vapors can influence the abundance, composition and proper- ties of secondaryorganicaerosol (SOA). Only recently have SOA models been developed that explicitly represent multi- generational SOA formation. In this work, we integrated the statistical oxidation model (SOM) into SAPRC-11 to simu- late the multi-generational oxidation and gas/particle parti- tioning of SOA in the regional UCD/CIT (University of Cal- ifornia, Davis/California Institute of Technology) air quality model. In the SOM, evolution of organic vapors by reaction with the hydroxyl radical is defined by (1) the number of oxygen atoms added per reaction, (2) the decrease in volatil- ity upon addition of an oxygen atom and (3) the probability that a given reaction leads to fragmentation of the organic molecule. These SOM parameter values were fit to labora- tory smog chamber data for each precursor/compound class. SOM was installed in the UCD/CIT model, which simulated air quality over 2-week periods in the South Coast Air Basin of California and the eastern United States. For the regions and episodes tested, the two-product SOA model and SOM produce similar SOA concentrations but a modestly different SOA chemical composition. Predictions of the oxygen-to- carbon ratio qualitatively agree with those measured globally using aerosol mass spectrometers. Overall, the implementa- tion of the SOM in a 3-D model provides a comprehensive framework to simulate the atmospheric evolution of organicaerosol.
KM-GAP Model and Parameters. A kinetic multi-layer model of gas- particle interactions in aerosols and clouds (KM-GAP) (1) is used for simulations. For size-resolved simulations, the bin method with full-moving size structure is used, in which the number concentration of particles in each size bin is conserved but the single particle volumes change (2). The number of size bins is 20 in this study. Coagulation is not considered in the model, as the coagulation timescale of more than a day signiﬁcantly exceeds the experimental timescale (3, 4). KM-GAP consists of multiple model compartments and layers, respectively: gas phase, near- surface gas phase, sorption layer, surface layer, and a number of bulk layers. KM-GAP treats the following processes explicitly: gas-phase diffusion, gas-surface transport (reversible adsorp- tion), surface-bulk exchange, bulk diffusion, and a selection of chemical reactions in the gas and particle phases. Note that as- sumptions of instantaneous gas-particle partitioning and homo- geneous mixing of the particle bulk, which are often assumed in secondaryorganicaerosol (SOA) modeling studies (5), were not applied. Surface and bulk layers can either grow or shrink in response to mass transport, which eventually leads to particle growth or shrinkage. Surface-bulk transport and bulk diffusion are treated as mass transport from one bulk layer to the next through ﬁrst-order transport velocities, which are calculated from the bulk diffusion coefﬁcients (1). As the experiments considered here were conducted under dry conditions, ammo- nium sulfate is assumed to remain in the form of crystalline seed particles on which SOA condenses. The ammonium sulfate core is represented by one bulk layer, and the organic phase is re- solved with 10 bulk layers. Ideal mixing is assumed within the organic phase (mole fraction-based activity coefﬁcients are as- sumed to be unity), an assumption that is reasonable for an SOA phase formed by the oxidation products of a single parent com- pound (here dodecane) at conditions of low water content (low relative humidity) (6). Loss of gas-phase semivolatile organic compounds (SVOCs) to the chamber wall (4, 7) is considered using a pseudo-ﬁrst order gas-phase wall-loss coefﬁcient k w (see
Secondaryorganicaerosol (SOA) constitutes a significant fraction of total atmospheric particulate loading, but there is evidence that SOA yields based on laboratory studies may underestimate at- mospheric SOA. Here we present chamber data on SOA growth from the photooxidation of aromatic hydrocarbons, finding that SOA yields are systematically lower when inorganic seed particles are not initially present. This indicates that concentrations of semivolatile oxidation products are in- fluenced by processes beyond gas-particle partitioning, such as chemical reactions and/or loss to chamber walls. Predictions of a kinetic model in which semivolatile compounds may undergo re- actions in both the gas and particle phases in addition to partitioning are qualitatively consistent with the observed seed effect, as well as with a number of other recently observed features of SOA formation chemistry. The behavior arises from a kinetic competition between uptake to the particle phase and reactive loss of the semivolatile product. It is shown that when hydrocarbons react in the absence of preexisting organicaerosol, such loss processes may lead to measured SOA yields lower than would occur under atmospheric conditions. These results underscore the need to conduct studies of SOA formation in the presence of atmospherically relevant aerosol loadings.
Laboratory chambers, invaluable in atmospheric chemistry and aerosol formation 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.
Abstract. Within the framework of the global chemistry cli- mate model ECHAM–HAMMOZ, a novel explicit coupling between the sectional aerosol model HAM-SALSA and the chemistry model MOZ was established to form isoprene- derived secondaryorganicaerosol (iSOA). Isoprene oxida- tion in the chemistry model MOZ is described by a semi- explicit scheme consisting of 147 reactions embedded in a detailed atmospheric chemical mechanism with a total of 779 reactions. Semi-volatile and low-volatile compounds produced during isoprene photooxidation are identified and explicitly partitioned by HAM-SALSA. A group contribu- tion method was used to estimate their evaporation enthalpies and corresponding saturation vapor pressures, which are used by HAM-SALSA to calculate the saturation concentration of each iSOA precursor. With this method, every single precur- sor is tracked in terms of condensation and evaporation in each aerosol size bin. This approach led to the identifica- tion of dihydroxy dihydroperoxide (ISOP(OOH)2) as a main contributor to iSOA formation. Further, the reactive uptake of isoprene epoxydiols (IEPOXs) and isoprene-derived gly- oxal were included as iSOA sources. The parameterization of IEPOX reactive uptake includes a dependency on aerosol pH value. This model framework connecting semi-explicit isoprene oxidation with explicit treatment of aerosol tracers leads to a global annual average isoprene SOA yield of 15 % relative to the primary oxidation of isoprene by OH, NO 3
work demonstrates for modest concentrations of organic pre- cursor. Based solely on the precision of the extinction co- efficient measurements, the respective limits of detection (3σ ) of the three systems were respectively 1.2, 2.1, and 1.2 Mm −1 for the BBCRDS and CE-DOAS systems over 60 s, and for the IBBCEAS system over 5 s. Other recently developed broadband systems by Washenfelder et al. (2013) and Zhao et al. (2013) have reported somewhat lower preci- sions of around 0.2 Mm −1 (corresponding to a detection limit of 0.6 Mm −1 ). These figures represent the best case perfor- mance of the instruments and do not take into account any drift in the instruments’ baselines. Such instrumental drift was generally a larger source of uncertainty and limited the accuracy at small aerosol extinctions. Engineering improve- ments since the NO3Comp campaign, including more fre- quent re-calibration of the baseline spectrum, have produced performances much closer to the best case values (Kennedy et al., 2011). Zhao et al. (2013) report a long-term stabil- ity of around 1 Mm −1 , which is only slightly worse than the detection limit of their instrument. The detection limits we report are well sufficient for monitoring aerosol extinction in polluted atmospheres (for instance, mean aerosol extinction coefficients are 121 Mm −1 in Atlanta, and over 300 Mm −1 in Beijing) and are possibly low enough for measurements in pristine environments (Carrico et al., 2003; He et al., 2009). If necessary, the detection limits of these broadband systems could be improved by increasing the cavity length or by using higher reflectivity cavity mirrors. As a point of comparison, a recently-developed broadband aerosol extinction spectrome- ter using a multipass White cell had a higher detection limit of 33 Mm −1 (albeit over a very wide spectral range of 250– 700 nm) compared to the optical cavity instruments in this work (Chartier and Greenslade, 2012). It should be noted that detection limits of cavity ring-down systems are typi- cally well below 1 Mm −1 (Moosmüller et al., 2009); never- theless, for the broadband instruments, the aerosol extinction is typically obtained in addition to the quantification of trace gases.
Jimenez, J. L., Canagaratna, M. R., Donahue, N. M., Prevot, A. S. H., Zhang, Q., Kroll, J. H., DeCarlo, P. F., Allan, J. D., Coe, H., Ng, N. L., Aiken, A. C., Docherty, K. S., Ulbrich, I. M., Grieshop, A. P., Robinson, A. L., Duplissy, J., Smith, J. D., Wil- son, K. R., Lanz, V. A., Hueglin, C., Sun, Y. L., Tian, J., Laakso- nen, A., Raatikainen, T., Rautiainen, J., Vaattovaara, P., Ehn, M., Kulmala, M., Tomlinson, J. M., Collins, D. R., Cubison, M. J., E., Dunlea, J., Huffman, J. A., Onasch, T. B., Alfarra, M. R., Williams, P. I., Bower, K., Kondo, Y., Schneider, J., Drewnick, F., Borrmann, S., Weimer, S., Demerjian, K., Salcedo, D., Cottrell, L., Griffin, R., Takami, A., Miyoshi, T., Hatakeyama, S., Shi- mono, A., Sun, J. Y., Zhang, Y. M., Dzepina, K., Kimmel, J. R., Sueper, D., Jayne, J., Herndon, S. C., Trimborn, A. M., Williams, L. R., Wood, E. C., Middlebrook, A. M., Kolb, C., Baltensperger, U., and Worsnop, D. R.: Evolution of Organic Aerosols in the At- mosphere, Science, 326, 1525–1529, 2009.
Abstract. Recent research has shown that secondaryorganic aerosols (SOA) are major contributors to ultrafine particle growth to climatically relevant sizes, increasing global cloud condensation nuclei (CCN) concentrations within the conti- nental boundary layer (BL). However, there are three recent developments regarding the condensation of SOA that lead to uncertainties in the contribution of SOA to particle growth and CCN concentrations: (1) while many global models con- tain only biogenic sources of SOA (with annual production rates generally 10–30 Tg yr −1 ), recent studies have shown that an additional source of SOA around 100 Tg yr −1 corre- lated with anthropogenic carbon monoxide (CO) emissions may be required to match measurements. (2) Many mod- els treat SOA solely as semi-volatile, which leads to con- densation of SOA proportional to the aerosol mass distribu- tion; however, recent closure studies with field measurements show nucleation mode growth can be captured only if it is assumed that a significant fraction of SOA condenses pro- portional to the Fuchs-corrected aerosol surface area. This suggests a very low volatility of the condensing vapors. (3) Other recent studies of particle growth show that SOA con- densation deviates from Fuchs-corrected surface-area con-
Figure 9 shows the change in (a) N3, (b) N10, (c) N40, and (d) N80 when changing LPJ-GUESS BVOC emissions from year 1000 to year 2000 with anthropogenic emissions off (BE2.AEO.LPJ–BE1.AEO.LPJ), providing an estimate for the aerosol changes when using an independent estimate of BVOC changes. Globally averaged, N3 and N10 increased by 5.9 and 3.5 %, respectively, whereas N40 and N80 de- creased by 0.1 and 1.8 %, respectively (see Table 2). The magnitude of the changes in N3 and N80 with the LPJ- GUESS simulations are the highest of all the simulations. This is due in part to the spatial variability in the LPJ-GUESS emission inventory when compared to the MEGAN emis- sion inventory, as well as lower total emissions. Similar to the comparable simulations using the MEGAN emissions (BE2.AEO.meg–BE1.AEO.meg; Fig. 6), there are increases in N3 over central North America, southern South America, eastern Australia, and central Eurasia exceeding 25 %. These regions correspond to regions of decreased BVOC emissions over the past millennium, which leads to decreases in SOA formation and increases in N3 (due to the deficit of con- densable material available to grow the smallest particles to CCN sizes). The same regions with significant increases in N3 also correspond to regions of significant decreases in CCN-sized particles. However, there are regions where the MEGAN simulations and the LPJ-GUESS simulations dif- fer. Even though LPJ-GUESS emits less BVOC emissions globally than MEGAN, the LPJ-GUESS simulations indi- cate higher-magnitude increases in N3 in the Northern Hemi- sphere than MEGAN. This is due to LPJ-GUESS emitting relatively more BVOCs in the northern boreal-forested re- gions than MEGAN (largely due to the different emission factors assumed for vegetation types and the treatment of the CO 2 response of the two emission models), and there-
Chapter 4 focuses further on the compilation of isoprene oxidation mechanisms for modeling purposes. The IEPOX mechanism in Chapter 3 is combined with those of other isoprene oxidation pathways from numerous additional studies (including those described in Appendices B-E regarding methyl vinyl ketone, methacryloyl per- oxynitrate, ISOPOOH, and isoprene ozonolysis) to create a new state-of-the-science explicit isoprene oxidation mechanism. With particular emphasis on the initial peroxy radical dynamics, oxidant budgets, and compounds known or suspected to contribute to organicaerosol formation, the explicit mechanism is presented pri- marily for use in box modeling, although an accompanying reduced mechanism condenses the model down to a size more manageable for chemical transport mod- els while retaining its most salient features. Future work will then incorporate this reduced mechanism into GEOS-Chem and, in a series of global simulations, exam- ine its effects on oxidant budgets, aerosol precursors, and small OVOCs of interest. Preliminary results show that the updated mechanism enhances NO x transport and reduces ozone formation compared to previous parameterizations, and substantially improves the model’s ability to accurately capture the relative importance of each isoprene peroxy radical isomer and its subsequent chemistry.
Flow tube reactors, with volumes typically in the range of 0.001–0.01 m 3 , provide aerosol residence times of seconds to minutes. Despite shorter residence times, much higher oxidant concentrations are attainable, which facilitate higher exposure times equivalent to 1–2 weeks of atmospheric oxi- dation. Further, experiments that may take hours in a smog chamber can be performed in minutes in a flow tube, under conditions that can be better controlled with respect to ox- idant concentration, contamination (Lonneman et al., 1981; 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 aerosol processes. The results of laboratory aerosol 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.
marily formed via glyoxal uptake (McNeill et al., 2012). This predominance of two aaSOA formation pathways in- volving relatively few species, compared to the total num- ber of aqueous compounds tracked by GAMMA, suggests that it is possible to model the majority of aqueous aerosol- phase SOA mass using a highly simplified reaction scheme, which is computationally efficient and suitable for coupling with larger-scale atmospheric chemistry models. GAMMA has therefore been used as a guide to develop a reduced mechanism for aaSOA formation, simpleGAMMA. sim- pleGAMMA reduces the total number of tracked aqueous species from 140 to 4 (glyoxal, IEPOX, 2-methyltetrol, and IEPOX organosulfate), with 2 species partitioning between the gas and aqueous aerosol phases (glyoxal and IEPOX), and a single aqueous-phase chemical process (reactive up- take of IEPOX), compared to 118 in GAMMA.