Top PDF Thermodynamic Modeling of Organic Aerosol

Thermodynamic Modeling of Organic Aerosol

Thermodynamic Modeling of Organic Aerosol

We present a vapor pressure estimation method, based on quantum chemistry methods, to predict the liquid vapor pressure, enthalpies of vaporization, and heats of sublimation of atmospheric organic compounds. Predictions are compared to literature data, and the overall accuracy is considered satisfactory given the simplicity of the equations. Quantum mechanical methods were also used to investigate the thermodynamic feasibility of various acid-catalyzed aerosol-phase heterogeneous chemical reactions. A stepwise procedure is presented to determine physical properties such as heats of formation, standard entropies, Gibbs free energies of formation, and solvation energies from quantum mechanics, for various short-chain aldehydes and ketones. Equilibrium constants of hydration reactions and aldol condensation are then reported; predictions are in qualitatively agreement with previous studies. We have shown that quantum methods can serve as useful tools for first approximation, especially for species with no available data, in determining the thermodynamic properties of multifunctional oxygenates.
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Simplified aerosol modeling for variational data assimilation

Simplified aerosol modeling for variational data assimilation

The general circulation model of the Laboratoire de M´et´eorologie Dynamique (LMDz) in its version 3.3 sim- ulates the global life cycle for the main aerosol species, namely sea salt (SS), desert dust (DD), organic matter (OM), black carbon (BC) and sulphate (SU). Its standard version has a resolution of 3.75 ◦ in longitude, 2.5 ◦ in latitude, and 19 levels in the vertical with a hybrid σ -pressure coordinate with five of its levels under 850 hPa and nine above 250 hPa. The model computes the atmospheric transport with an Eu- lerian finite volume transport scheme for large scale advec- tion, a turbulent mixing scheme within the boundary layer and a mass flux scheme for convection. The time step for the dynamics equations is three minutes. The mass fluxes are accumulated over 5 time steps in order to apply the large scale advection every 15 min. The physical and chemical parametrisation are applied every 30 min. An operator split- ting technique is applied to the different processes that affect the prognostic variables (Boucher et al., 2002).
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Regional Air Quality and Climate Modeling using WRF/Chem with Improved Model Representations of Organic Aerosol Formation and Aerosol Activation.

Regional Air Quality and Climate Modeling using WRF/Chem with Improved Model Representations of Organic Aerosol Formation and Aerosol Activation.

17 2011). Some of the missing key precursors that are not treated in some models include semi- volatile primary organic aerosol (POA), long-chain n-alkanes, polycyclic aromatic hydrocarbons (PAHs), large olefins that have lower volatilities compared to traditional SOA precursors (Chan et al., 2009), as well as glyoxal (Volkamer et al., 2007). Glyoxal is receiving increased attention due to its contribution to SOA. Because of its high effective Henry’s law coefficient, it is efficiently taken onto aqueous aerosol droplets; it can also react with OH and other species in the gas or aerosol-phases to form other organic compounds (Galloway et al., 2011). It is also a significant intermediate species from aromatic hydrocarbon photooxidation under humid conditions for SOA formation (Nakao et al., 2012). PAHs are another important contributor to SOA. Napthalenes and methylnapthalenes are found to account for up to 54% of the total SOA from oxidation of diesel emissions, which is a potentially large source of urban SOA (Chan et al., 2009). Zhang and Ying (2012) in a modeling study using the CMAQ model showed that PAHs from anthropogenic sources can produce approximately 4% of total anthropogenic SOA.
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On the Physicochemical Processes Controlling Organic Aerosol Hygroscopicity.

On the Physicochemical Processes Controlling Organic Aerosol Hygroscopicity.

Ambient aerosols take up water in humid air and serve as condensation nuclei for haze and cloud droplets. Uncertainty in aerosol water uptake contributes to the uncertainty in estimates of aerosol water content and hinders studies that extrapolate measured hygroscopic growth factors to predict a particles’ ability to serve as a cloud condensation nucleus (Prenni et al., 2007). Aerosol water content is determined by the ambient relative humidity (RH) and the associated chemical composition dependent hygroscopicity of the aerosol. Particle hygroscopicity is a measure that scales the volume of water associated with a unit volume of dry particle (Petters and Kreidenweis, 2007) and depends on the molar volume and the activity coefficients of the dissolved compounds (Christensen and Petters, 2012). Activity coefficients cannot be obtained by modeling alone because non-ideal interactions between compounds remain largely unexplained on the molecular level. One of the main difficulties is the lack of experimental data to constrain activity coefficient models (Amundson et al., 2007; Topping et al., 2005) for likely constituents of atmospheric aerosols (Raatikainen and
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The AeroCom evaluation and intercomparison of organic aerosol in global models

The AeroCom evaluation and intercomparison of organic aerosol in global models

Kanakidou et al. (2005) reviewed how organic aerosols were incorporated into global chemistry transport models (CTMs) and general circulation models (GCMs), and identified gaps in knowledge that deserved further investigation. The POA sources include fossil fuel, biofuel and biomass burning, as well as the less understood sources of marine OA, bi- ological particles and soil organic matter on dust (Kanaki- dou et al., 2012, and references therein). Biogenic VOCs (BVOCs) greatly contribute to OA formation (e.g., Griffin et al., 1999b; Kanakidou et al., 2012), implying that sig- nificant feedbacks exist between the biosphere, the atmo- sphere and climate that affect the OA levels in the atmo- sphere, which was also demonstrated by more recent stud- ies (Tsigaridis et al., 2005; Arneth et al., 2010; Carslaw et al., 2010; Paasonen et al., 2013). In addition, oxidant and pollutant enhancement by human-induced emissions is ex- pected to increase OA levels, even those chemically formed by BVOC (Hoyle et al., 2011, and references therein); it is therefore conceivable that some portion of the ambient bio- genic SOA, which would had been absent under preindus- trial conditions, can be removed by controlling emissions of anthropogenic pollutants (Carlton et al., 2010). Goldstein and Galbally (2007) estimated that SOA formation could be as high as 910 TgC a −1 , which is at least an order of mag- nitude higher than any SOA formation modeling study, as shown here. Spracklen et al. (2011) were able to reconcile AMS observations (mostly from the Northern Hemisphere mid-latitudes during summer) with global CTM simulations by estimating a large SOA source (140 Tg a −1 ). 100 Tg a −1 was characterized as anthropogenically controlled, 90 % of which was possibly linked to anthropogenically enhanced SOA formation from BVOC oxidation. Similar conclusions were reached by Heald et al. (2011) by comparing aircraft AMS observations of submicron OA with the results of an- other global model, and by Heald et al. (2010) by account- ing for the satellite-measured aerosol optical depth that could possibly be due to OA. Recently, Carlton and Turpin (2013) showed that anthropogenically enhanced aerosol water in the eastern USA could lead to an increase in WSOC from
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Studies of Ambient Organic and Inorganic Aerosol in Southern California

Studies of Ambient Organic and Inorganic Aerosol in Southern California

based on updated chamber data, a growing body of evidence suggests that the assumptions on which the Odum 2-product framework is based are simply not valid under ambient conditions (e.g. in- stantaneous, reversible thermodynamic equilibration, lack of multigenerational oxidation pathways, lack of fragmentation pathways, and lack of particle-phase reaction pathways). Therefore, once vapor-phase wall loss has been accounted for in a new suite of laboratory chamber experiments, the SOA parameterizations within atmospheric chemical transport models should also be updated. Specifically, significant progress has been made in using box models to represent the formation and evolution of SOA. However, little work has focused on bridging the gap between the advanced chem- ical mechanisms of 2D SOA box models and the strict computational requirements of 3D CTMs. In Chapter 3, a novel mathematical framework, termed the Oxidation State/Volatility Moment Method, designed to address this issue is presented. Various numerical experiments suggest that the Moment Method is a promising mathematical framework for combining 2D SOA models with 3D CTMS. However, the Numeric Drift Test revealed that inexact fitting of symmetric distributions to skewed distributions introduces an unacceptable amount of error when the DEF procedure is re- peated several times. Two strategies (Moment-Bin Hybrid & Grid Coarsening) for overcoming this numerical drift limitation were discussed qualitatively. Future work should focus on (1) removing the error introduced by inexact fitting followed by thermodynamic partitioning, (2) assessing how the Moment Method performs when gas-phase oxidation/fragmentation chemistry is included, (3) testing alternative 1D probability distributions (not just log-normal and gamma distributions), and (4) quantitatively assessing the Moment-Bin Hybrid & Grid Coarsening approaches as compared to the explicit 2D-VBS.
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Electrodynamic balance measurements of thermodynamic, kinetic, and optical aerosol properties inaccessible to bulk methods

Electrodynamic balance measurements of thermodynamic, kinetic, and optical aerosol properties inaccessible to bulk methods

a mole fraction of 0.025 at saturation), so that the water ac- tivity of the saturated solution is close to 1. Shikimic acid is a carboxylic acid which has been detected in biomass burn- ing aerosol (Medeiros and Simoneit, 2008). In recent years it has been shown that atmospheric organic matter can adopt an amorphous solid state (Zobrist et al., 2008; Virtanen et al., 2010). In the case that organic aerosols host chemical reac- tions, the gradual decrease in diffusivity from liquid to amor- phous solid state is likely to cause a reduction in reaction rate, an effect well known for crystalline material (Moise and Rudich, 2002). First investigations of this effect for amor- phous material have been made by Shiraiwa et al. (2011), Kuwata and Martin (2012) and Zhou et al. (2013). As we are using the ozonolysis of shikimic acid as a model system to investigate the interplay of physical and chemical kinetics (Steimer et al., 2014), we need a detailed understanding of how relative humidity affects the physical state and diffusiv- ity of water in shikimic acid. Additionally, the dependence of the concentration on water activity has to be known to de- termine initial bulk phase concentrations of shikimic acid for rate law assessments.
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Near-infrared laser desorption/ionization aerosol mass spectrometry for measuring organic aerosol at atmospherically relevant aerosol mass loadings

Near-infrared laser desorption/ionization aerosol mass spectrometry for measuring organic aerosol at atmospherically relevant aerosol mass loadings

Several recent experimental studies have shown clearly the importance of characterizing SOA under atmospherically rel- evant organic particle mass loading. Shilling et al. (2009) characterized SOA particles derived from the dark ozonol- ysis of α-pinene using an Aerodyne high-resolution time- of-flight aerosol mass spectrometer (HR-ToF-AMS) (Cana- garatna et al., 2007; DeCarlo et al., 2006) coupled to a continuous-flow chamber. That study featured a wide-range of organic particle loadings (ca. 0.5 to > 140 µg m −3 ) and the mass spectra suggested more oxygenated organic mate- rial (i.e. a higher O/C atomic ratio and density) in SOA at lower loading, which also had greater compositional variabil- ity compared to higher loadings typically employed in tradi- tional chamber experiments. As noted in that work (Shilling et al., 2009), these experiments suggest that the extrapolation of results derived from chamber-based studies obtained un- der relatively high loading of the organic particle phase may be inappropriate for modeling SOA formation in the atmo- sphere where lower loadings are typical.
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Secondary Organic Aerosol Composition Studies Using Mass Spectrometry

Secondary Organic Aerosol Composition Studies Using Mass Spectrometry

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 secondary organic aerosol (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
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Mass Spectrometric Analysis of Organic Aerosol Composition: Laboratory and Ambient

Mass Spectrometric Analysis of Organic Aerosol Composition: Laboratory and Ambient

Organic matter is ubiquitous in atmospheric aerosols and ac- counts for a major fraction of particulate matter mass (Zhang et al., 2007a). Most particulate organic matter (POM) is sec- ondary in origin, comprising condensable oxidation products of gas-phase volatile organic compounds (VOCs) (Hallquist et al., 2009). Despite the importance of secondary organic aerosol (SOA), its sources and formation processes are not fully understood. Global modeling studies predict that oxi- dation of biogenic hydrocarbons dominates the global SOA burden owing to high emissions and efficient SOA produc- tion (Chung and Seinfeld, 2002; Kanakidou et al., 2005; Henze and Seinfeld, 2006). This is supported by observa- tions of high levels of modern (hence biogenic) carbon in am- bient particulate organic matter, even in urban centers such as Nashville, TN, Tampa, FL and Atlanta, GA (Lewis et al., 2004; Lewis and Stiles, 2006; Weber et al., 2007). However, field observations have repeatedly shown that SOA forma- tion is highly correlated with anthropogenic tracers, such as CO and acetylene (de Gouw et al., 2005, 2008).
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Measuring the atmospheric organic aerosol volatility distribution: a theoretical analysis

Measuring the atmospheric organic aerosol volatility distribution: a theoretical analysis

Abstract. Organic compounds represent a significant frac- tion of submicrometer atmospheric aerosol mass. Even if most of these compounds are semi-volatile in atmospheric concentrations, the ambient organic aerosol volatility is quite uncertain. The most common volatility measurement method relies on the use of a thermodenuder (TD). The aerosol passes through a heated tube where its more volatile compo- nents evaporate, leaving the less volatile components behind in the particulate phase. The typical result of a thermode- nuder measurement is the mass fraction remaining (MFR), which depends, among other factors, on the organic aerosol (OA) vaporization enthalpy and the accommodation coeffi- cient. We use a new method combining forward modeling, introduction of “experimental” error, and inverse modeling with error minimization for the interpretation of TD measure- ments. The OA volatility distribution, its effective vaporiza- tion enthalpy, the mass accommodation coefficient and the corresponding uncertainty ranges are calculated. Our results indicate that existing TD-based approaches quite often can- not estimate reliably the OA volatility distribution, leading to large uncertainties, since there are many different combi- nations of the three properties that can lead to similar ther- mograms. We propose an improved experimental approach combining TD and isothermal dilution measurements. We evaluate this experimental approach using the same model, and show that it is suitable for studies of OA volatility in the lab and the field.
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Investigating Sources and Sinks of Organic Aerosols: NO₃ initiated Oxidation of Isoprene and Heterogeneous Oxidation of Organic Aerosol

Investigating Sources and Sinks of Organic Aerosols: NO₃ initiated Oxidation of Isoprene and Heterogeneous Oxidation of Organic Aerosol

To fully elucidate the relationship between the actual products identified and those inferred from fitting the yield data would require a modeling study that is beyond the scope of this work. However, we emphasize that there are large uncertainties associated with the quantification of peroxides and it is likely that their contributions to total SOA can be overestimated. Indeed, if the mass yield for these nonvolatile peroxides were lower (for instance, ~2%), this would agree well with the observed yield curve behavior. The measurement of peroxides certainly warrants further study. This work serves as a good example in showing that caution must be taken when interpreting experiments with low aerosol yields, especially when a relatively minor pathway may be responsible for forming the aerosols.
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Modeling regional air quality and climate: improving organic aerosol and aerosol activation processes in WRF/Chem version 3.7.1

Modeling regional air quality and climate: improving organic aerosol and aerosol activation processes in WRF/Chem version 3.7.1

Abstract. Air quality and climate influence each other through the uncertain processes of aerosol formation and cloud droplet activation. In this study, both processes are im- proved in the Weather, Research and Forecasting model with Chemistry (WRF/Chem) version 3.7.1. The existing Volatil- ity Basis Set (VBS) treatments for organic aerosol (OA) formation in WRF/Chem are improved by considering the following: the secondary OA (SOA) formation from semi- volatile primary organic aerosol (POA), a semi-empirical for- mulation for the enthalpy of vaporization of SOA, and func- tionalization and fragmentation reactions for multiple gener- ations of products from the oxidation of VOCs. Over the con- tinental US, 2-month-long simulations (May to June 2010) are conducted and results are evaluated against surface and aircraft observations during the Nexus of Air Quality and Climate Change (CalNex) campaign. Among all the config- urations considered, the best performance is found for the simulation with the 2005 Carbon Bond mechanism (CB05) and the VBS SOA module with semivolatile POA treatment, 25 % fragmentation, and the emissions of semi-volatile and intermediate volatile organic compounds being 3 times the original POA emissions. Among the three gas-phase mech- anisms (CB05, CB6, and SAPRC07) used, CB05 gives the best performance for surface ozone and PM 2.5 concentra-
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Investigations of Global Chemistry-Climate Interactions and Organic Aerosol Using Atmospheric Modeling

Investigations of Global Chemistry-Climate Interactions and Organic Aerosol Using Atmospheric Modeling

Since the winter organic aerosol is dominated by SVOC aerosol, further examination of winter- time concentrations allows us to assess the model performance while minimizing uncertainties in the biogenic aerosol parameterization as a source of discrepancy. Figure 3.7 shows winter (December- January-February 2000) simulated and observed aerosol OC concentrations over the US. Surface measurements of total OC from the Interagency Monitoring of Protected Visual Environments (IM- PROVE) network (http://vista.cira.colostate.edu/improve/) are overlaid in circles on top of the simulated OC concentrations. Since the IMPROVE network observations are reported as mass of carbon rather than the mass of total organic aerosol, converting model values, which tend to be in total organic mass, to organic carbon mass is an additional source of potential discrepancy. Since the aerosol from SVOCs is tracked in GEOS-Chem as the mass of carbon and OM/OC ratios of 1.4 for POA and 2.1 for SOA are applied in the partitioning routines, focusing on the wintertime, where aerosol is dominated by SVOC sources with a model imposed OM/OC ratio, can reduce the effect of the OM/OC ratio which must be specified for traditional and IVOC SOA. To convert traditional and IVOC SOA to OC, an OM/OC ratio of 2.1 is used and is roughly consistent with chamber data with the notable exception of α-pinene SOA (Chhabra et al., 2010). Comparisons to the IMPROVE network by Liao et al. (2007) indicate that GEOS-Chem underpredicts annual OA levels by about 0.56 µg/m 3 (mean bias) or 34% (normalized mean bias) with the bias being larger in magnitude in
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Modeling the Effect of Vapor Wall Deposition on the Formation of Secondary Organic Aerosol in Chamber Studies

Modeling the Effect of Vapor Wall Deposition on the Formation of Secondary Organic Aerosol in Chamber Studies

subset, and M init is the mass concentration of any initially present absorbing organic concentration, all expressed in terms of µg m –3 of air. In general, G i eq varies for each size bin, based on the mass concentration of species i and the total organic concentration in that size bin. For computational convenience, owing to the presence of coagulation, the concentration of each organic species i in each size bin is not tracked dynamically; only the total condensed mass of each species over the entire size distribution is determined. The total mass in each size bin is also tracked, but this mass is not resolved into organic and inorganic masses because the number of particles in each size bin changes with time. Consequently, G i eq is calculated globally over the entire size distribution, based on the total mass of condensed species i and the total mass of condensed organics. (The total amount of species i in the condensed phase includes, as noted, the mass condensed onto particles that subsequently deposited on the wall.) We validated that this simplification of the actual size- dependent concentration dynamics captures the basic dependence of SOA yield on aerosol surface area by creating an equivalent moving bin model without coagulation, in which the total number of particles in each bin is conserved. The concentrations of each species i in each bin are then used to calculate G i eq for each bin. SOA yields predicted in this manner are virtually identical to those of the fixed bin model. A nominal (0.01 µg m –3 ) nonvolatile initial organic seed aerosol concentration, M init , is assumed to be present in the chamber regardless of initial inorganic seed number concentration merely to avoid numerical errors in Equation 2.6 at the first time step. Results are insensitive to this value up to 1 µg m –3 . Simulations (not shown) demonstrate that including the Kelvin effect in the calculation of the equilibrium vapor pressure has a negligible influence on the computed SOA yields for size distributions typical of seeded SOA chamber experiments.
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Chamber Studies and Modeling of Secondary Organic Aerosol Formation

Chamber Studies and Modeling of Secondary Organic Aerosol Formation

We emphasize that the mechanism shown in Fig. 2.2d, while possibly valid for some semivolatile organics, is not intended as a general scheme describing the chemistry of all condensable compounds. Most SOA-forming reactions involve a very large number of semivolatiles, which may react via any number of mechanisms, including those shown in Fig. 2.2. This mechanism is instead used as a model system illustrating the potential influence of reactions of semivolatile species on SOA formation, and the resulting dependence of SOA yields on reaction and partitioning conditions. Such reactions are often not treated explicitly in models of SOA formation, but certainly occur for a wide range of compounds: most organics are susceptible to reaction with OH, and loss to chamber walls is a potential sink for species that are efficiently taken up to the aerosol phase. If these processes are fast on the timescale of chamber experiments, they may have a substantial impact on SOA growth, and on the relationship between chamber studies and real atmospheric conditions. Only by explicitly including such reactions in models of SOA formation, or by making SOA yield measurements under oxidative and partitioning conditions relevant to the atmosphere, can such effects be taken fully into account.
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Studies of Chamber Organic Aerosol using an Aerodyne High Resolution Time of Flight Aerosol Mass Spectrometer

Studies of Chamber Organic Aerosol using an Aerodyne High Resolution Time of Flight Aerosol Mass Spectrometer

organic/sulfate ratio after 4 h in the work by Liggio et al. (2005a) is ∼16, which is more than an order of magnitude larger than in this study (∼0.35), and we expect that the difference would have been even larger if we had employed the lower GL mixing ratios of the work by Liggio et al. (2005a,b). Although the first two hours of our data can be interpreted with a linear slope, the curvature of 2-4 hrs in the study presented here shows a decreasing uptake rate in agreement with the fact that equilibrium is achieved after about 10 h. It is hard to interpret a clear curvature in the work by Liggio et al. (2005a). The average aerodynamic diameter of the seed aerosol in the study by Liggio et al. (2005a) is initially smaller, and thus the uptake rate per volume of seed aerosol is expected to be faster than in this study, but it is unlikely that this alone can explain the discrepancy. In a more recent study, Volkamer et al. (2009) showed that photochemical uptake was higher within 90 seconds than over several hours in our study and the study of Kroll et al. (2005). The presence of OH in the study by Volkamer et al. (2009) represents a significant difference, which could explain the large difference in uptake rate. However, we also currently cannot determine a particular reason why the non-irradiated studies potentially show two different types of uptake (irreversible and reversible) at different rates, information that is critical if understanding of these processes is to be applied to ambient studies.
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Isoprene-derived secondary organic aerosol in the global aerosol–chemistry–climate model ECHAM6.3.0–HAM2.3–MOZ1.0

Isoprene-derived secondary organic aerosol in the global aerosol–chemistry–climate model ECHAM6.3.0–HAM2.3–MOZ1.0

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 secondary organic aerosol (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
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Primary to secondary organic aerosol: evolution of organic emissions from mobile combustion sources

Primary to secondary organic aerosol: evolution of organic emissions from mobile combustion sources

The time series of the POA factor mass concentration for experiment T63-1 is shown in grey in Fig. 2. Figure 2 also shows a predicted first-order POA wall loss curve in blue. Unlike the case shown in Fig. 1, the first-order POA loss does a poor job of representing the time trace of the PMF- derived POA factor for t > 0 h. The POA factor concentra- tion decreases faster than wall loss for the first 15 min after the start of photooxidation. This is due to the POA evaporat- ing slightly as the surrounding vapors are rapidly consumed (Robinson et al., 2007). The POA factor concentration subse- quently increases from 12.2 to 24.7 µg m −3 between t = 1 h and t = 2 h. This increase is driven by partitioning of POA vapors to the particle phase with increasing organic aerosol concentration (C OA ), and is not captured by the assumed
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simpleGAMMA v1.0 – a reduced model of secondary organic aerosol formation in the aqueous aerosol phase (aaSOA)

simpleGAMMA v1.0 – a reduced model of secondary organic aerosol formation in the aqueous aerosol phase (aaSOA)

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.
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