uct pinonic acid. As there are first- and second-generation products with m/z ( − )269, it is not clear how important this H-abstraction channel is. There have only been a few hy- droperoxides whose H-abstraction reaction rates and prod- uct distributions have been studied: methyl, ethyl and tert- butyl (Vaghjiani and Ravishankara, 1989; Niki et al., 1983; Wang and Chen, 2008; Baasandorj et al., 2010). It has been shown that the OO-H bond is fairly labile and H-abstraction from both the hydroperoxide as well as the α-carbon will oc- cur. H-abstraction from the hydroperoxyl group will result in reformation of the peroxy radical with the same branching ratio of products from the initial reaction forming, among other species, pinonaldehyde and hydroxy hydroperoxide. H-abstraction from the α-carbon of the hydroperoxy group would presumably form a hydroxy carbonyl, which is a mass analog of α-pinene hydroperoxide. In the model, the signal associated with α-pinene hydroperoxide is fit well with a production channel from α-pinene and a reaction rate with OH that is nearly as fast as α-pinene itself, suggesting that it is unlikely that there is a second-generation product formed at this mass. It is possible that following hydrogen abstrac- tion, the ring is cleaved, ultimately resulting in the forma- tion of pinonaldehyde. Finally, H-abstraction from either of the tertiary carbons will produce more highly oxygenated species, including a hydroxyl dihydroperoxide (m/z ( − )303). Of these different H-abstraction pathways, those involving the hydroperoxide and carbons α to the hydroperoxy and hy- droxyl groups are expected to dominate. The ring opened hy- droxy hydroperoxide has a highly reactive double bond in- stead of one of the tertiary carbons. After addition of OH to the double bond, the reaction will most likely proceed in one the following ways: formation of a dihydroxy dihydroperox- ide or intermolecular reaction of the alkyl radical with the hydroperoxy group forming a ring with one oxygen and re- leasing OH. The former product has not been observed in the present experiments; however, this highly oxidized com- pound has a low vapor pressure and is expected to primarily reside in the particle phase. The later product is a mass ana- log of the hydroxy hydroperoxide, and the two species are indistinguishable by CIMS.
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 organicaerosolformation, 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.
recent data analysis study using GAMMA (Sumner et al., 2014) suggested a possible role for photo-enhanced chem- istry in aaSOA formation by glyoxal involving organic pho- tosensitizers such as fulvic acid (Monge et al., 2012). This chemistry can be represented in simpleGAMMA by includ- ing irreversible glyoxal uptake with γ ∼ 10 −3 during sunlit hours, consistent with Fu et al. (2008), who based their rep- resentation on the experiments of Liggio et al. (2005), and with Waxman et al. (2013). A reactive uptake formulation was also used by Pye et al. (2013) to represent aaSOA for- mation by IEPOX. While reactive uptake may be the best alternative for representing unknown processes such as gly- oxal surface photochemistry, potential issues with reactive uptake formulations stem from the fact that they generally represent two or more physical processes (e.g., reversible up- take of VOCs followed by an aqueous-phase reaction) as one irreversible reactive uptake step. Lin et al. (2014) and Knote et al. (2014) found that a surface reactive uptake formulation for glyoxal led to significantly higher predicted SOA mass than a reversible multiphase representation of the chemistry. We previously predicted, using GAMMA, that glyoxal is the main contributor to aqueous aerosol-phase “brown carbon” formation by carbonyl-containing VOC precursors (Woo et al., 2013). Following that work, it is straightforward to track the formation of light-absorbing glyoxal derivatives in simpleGAMMA, with concentration-dependent aerosol light absorption calculated in post-processing. However, we note that fast photobleaching of aerosol brown carbon formed via this pathway has been demonstrated, limiting its potential impact on atmospheric chemistry and climate (Sa- reen et al., 2013; Woo et al., 2013; Lee et al., 2014). Code availability
Figure 11 (A) shows the simulated SOA growth (SIM.1) using the initial conditions in Exp. #2, together with the observed total organicaerosol mass as a function of reaction time and OH exposure. The model reproduces the chamber measured SOA yield at 3% RH when the conversion rate of 3 × 10 -3 s -1 is employed to represent the heterogeneous conversion of δ-hydroxycarbonyl to dihydrofuran. A second simulation (SIM.2) was run with the complete dihydrofuran chemistry removed while other parameters were held constant. The total organic mass is ~ 42% higher as a result after 18 h photooxidation. The formation of alkyl-substituted dihydrofuran from δ-hydroxycarbonyl is accompanied by an increase of vapor pressure from 5.36 × 10 -7 to 1.08 × 10 -4 atm at 300 K, as predicted by SIMPOL.1, and the total organic mass formed decreases. Although the addition of OH to the C=C double bond in the substituted dihydrofuran introduces an extra OH group, the decrease of vapor pressure owing to the addition of one OH group does not compensate for the heterogeneous conversion of both –C=O and –OH groups in δ -hydroxycarbonyl to an –O– group in a non-aromatic ring in dihydrofuran. The predicted the average carbon oxidation state is ~ 7 – 15% higher than observations. The overprediction is within the uncertainties in the O:C (31%) and H:C (10%) measurement by AMS (Aiken et al., 2008). Incorporation of the substituted dihydrofuran formation and removal pathways in the model leads to an increase in the simulated OS C . Compared with
glyoxal uptake and fast photochemical uptake that results only from residual organics in the chamber. The oxidized OA fraction (carboxylic acids) was not attributable to glyoxal (Figure 3c) in our uptake experiments with glyoxal and a gas ‐ phase OH source. This is in contrast to the lab- oratory studies of bulk aqueous oxidation of glyoxal by OH with a condensed ‐ phase OH source, which saw photo- chemical products, specifically carboxylic acids [Carlton et al., 2007; Tan et al., 2009]. The corrected m/z 44 (carbox- ylic acid) signal indicates the presence of oxidation pro- ducts, but blank experiments show that this is not a result of glyoxal uptake but from residual chamber organics. With the exception of the oxidized aerosol fraction, which exhi- bits no dependence on glyoxal, the glyoxal ‐ dependent growth rate and composition of the aerosol as judged by the AMS are identical in the presence and absence of OH. Although the AMS fragments both oligomers and other higher molecular weight compounds, previous experiments have clearly shown that glyoxal oligomers can be detected [Galloway et al., 2009; Liggio et al., 2005]. If OH affected the oxidation or oligomerization chemistry in the aerosol, a shift to higher masses would be evident in the overall AMS mass spectra when compared to dark uptake conditions. Analysis of the m/z 105 to m/z 58 ratio rules out that OH influences the formation of glyoxal (acetal) oligomers. Our analysis also shows that the overall mass spectra of photo- chemical glyoxal uptake are not shifted to higher molecular weights or do not indicate other changes compared to dark uptake. In addition, analysis with particle‐into‐liquid‐samplers
The environmental chamber is used to isolate atmospheric chemistry under well- controlled conditions. Both gas-phase chemistry and secondaryorganicaerosol (SOA) formation and growth are studied in such chambers. SOA is formed when volatile organic compounds (VOCs) undergo oxidation to form low volatility prod- ucts that subsequently partition into the particle phase. Numerous environmental chambers have been constructed and are in use worldwide. The science underly- ing the environmental chamber can be divided into four parts: (1) design of the chamber; (2) characterization of the chamber; (3) execution of experiments; and (4) interpretation of the data. The purpose of this chapter is to discuss each of these aspects so as to elucidate the considerations in the use of an environmental chamber to perform studies of atmospheric chemistry and aerosolformation. A critical aspect of environmental chamber experiments is the suite of instrumentation used to characterize the gas and particle phases in the chamber. We will address the measurement of particle size distributions in chambers; a number of reviews of gas- and particle-phase chemical composition measurements exist, so we do not address these here.
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.
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.
I am especially grateful to Tomtor and Roya for their patience in teaching me so many things in the lab, from little things like “the difference between a back ferrule and a front ferrule”, to how to operate the many instruments, and for always being there to help me even when they were overwhelmed with their own work. The two and a half years with Jesse in the lab have been incredible. We built our friendship through running hundreds and hundreds of chamber experiments (final checks!), and together we survived the ups and downs of the experiments. Jesse taught me so much about atmospheric chemistry and I am grateful for his patience and guidance. I considered myself extraordinarily lucky to have the opportunity to work so closely with such a great scientist and I treasure our friendship dearly.
OR) by treating the SOM as a user-defined function. The best fit was determined as that which gave the best agree- ment between simulated and observed SOA concentrations as a function of time and where OA concentrations had been corrected for particle wall losses. The curve fitting tool used the Levenberg–Marquardt algorithm to minimize the Chi- square parameter. While important, the fitting did not con- sider the influence of organic gas/vapor losses to the cham- ber walls (Zhang et al., 2014) and hence the fitted parame- ters represent the minimum potential of the precursor to form SOA; the influence of gas/vapor wall losses on the SOM pa- rameters and consequently on regional SOA concentrations will be explored in a follow-up study. The fitting was under- taken assuming a monodisperse particle size distribution that matched the aerosol surface area in the chamber experiment and an accommodation coefficient of 1. Using an accommo- dation coefficient of 1 or 0.1 did not dramatically change the fitted parameters since the timescale to achieve gas/particle equilibrium is less than a few minutes for these conditions and much faster than the timescale of SOA formation in these experiments (Zhang et al., 2014; McVay et al., 2014).
In this section, the model is briefly described. This begins with a brief description of the default configuration, fol- lowed by the model developments made in this study. The chemistry–climate model used in this study is the United Kingdom Chemistry and Aerosol (UKCA) model (Morgen- stern et al., 2009; Mann et al., 2010; O’Connor et al., 2014) which is coupled to the Global Atmosphere 4.0 (GA4.0) configuration (Walters et al., 2014) of the Hadley Centre Global Environmental Model (Hewitt et al., 2011) version 3 (HadGEM3). The atmosphere-only configuration with pre- scribed sea surface temperature and sea ice fields based on 1995–2004 reanalyses data (Reynolds et al., 2007) was used. The model was run at a horizontal resolution of N96 (1.875 ◦ longitude by 1.25 ◦ latitude) with 85 terrain-following hybrid- height levels distributed from the surface to 85 km. Horizon- tal winds and temperature in the model were nudged towards ERA-Interim reanalyses for the 1999–2000 period (Dee et al., 2011) using a Newtonian relaxation technique with a re- laxation time constant of 6 h (Telford et al., 2008). There was no feedback from the chemistry or aerosols onto the dynam- ics of the model; this ensured identical meteorology across all simulations so that differences in SOA were solely due to differences in precursor oxidation mechanisms and deposi- tion.
The TSAR and PAM reactors differ in volume, geome- try, flow conditions and residence time. The most signifi- cant difference is in the oxidation process: TSAR operates in OFR254 mode and PAM in OFR185 mode. However, the agreement between yields and organic mass spectra of SOA produced in both the TSAR and PAM reactors show that the oxidation products are similar in both reactors, at least in the case of toluene. In OFR254, the sample is first exposed to ozone (before the oxidation reactor) and then to both ozone and OH radicals. If the VOCs in the sample react fast with ozone, the resulting SOA mass might differ between OFR254 and OFR185. This was not the case for toluene, as dark ex- periments (only ozone and no UV light) did not produce any secondary mass. In other applications, for example when oxi- dizing biogenic precursors which are highly reactive towards ozone, the results between OFR254 and OFR185 presum- ably differ, with OFR185 being more realistic as the sample is exposed to ozone and OH simultaneously. However, the main application of TSAR is to measure vehicle emissions, which are more reactive towards OH than ozone (Gentner et al., 2012; Tkacik et al., 2014). The potential of ozone to pro- duce SOA from the emission can be measured by injecting ozone into TSAR with UV lights turned off.
Beyond being able to model the fraction of oligomers formed due to the presence of inorganic aerosols, an experimental oligomer mass fraction estimation technique was developed using TGA which is targeted to bulk phase analysis of aerosols. The TGA method development study resulted in a new approach thermal analysis for aerosols. By subtracting the inorganic aerosol signal, the evolution of the particle phase organics with response to temperature increase is captured. The TGA study not only resulted in the development of a new analytical method, but also provide valuable information as to the how inorganics influence specific types of molecules. Under acidic conditions alcohols were found to only form organic sulfate, while pinonic acid which contains and a carboxylic acid group and a ketone was able to form a small amount of oligomer. Also the TGA study revealed a possible flaw in the use of organic carbon, elemental carbon (OC/EC) thermal analysis. OC/EC analysis is a thermally based analysis technique which heats sampled aerosols, and analyzes the evolved gases. Typically organic carbon is quantified at temperatures as high as 500ºC (Chow et al. 2004), and the charring of organics are assumed to occur in the presence of oxygen gas. The TGA study showed that at temperatures above 250ºC the oligomer fraction of the aerosol begins to decompose and form char. This char is quantified as EC in OC/EC analysis causing a possible underestimation of the OC fraction of aerosols.
SOA particles exist in a chemical and physical state of flux: the constituent molecules can continue to condense, evaporate, and be chemically transformed through reactions and photochemistry. The dynamic nature of gas/particle partitioning dictates that compounds can freely partition between the phases, driven by concentration gradients and temperature. Chemical reactions and photochemical reactions can occur within the bulk and at the surface of aerosol. Oxidation in the particle-phase can occur at the surface or from unique bulk processes like photo-Fenton reactions. Oligomerization is a key process in particle formation and growth; it has been shown to be important to anthropogenic and biogenic compounds. These transformative processes occur on an atmospherically relevant timescale. Detailed chemical characterization of SOA composition at a molecular level is typically conducted using solvent extraction and electron impact ionization (EI) in order to conduct a mass spectrometric analysis. These techniques are highly useful in identifying molecules by fragmentation patterns, which indicate structural details. Analysis method artifacts arise with the use of these methods, however; less stable compounds like peroxides, epoxides, and acid-cleavable, high-boiling oligomers are not readily detectable in their native state. Derivatization and gas chromatographic/mass spectrometric (GC/MS) analysis, and liquid chromatography/mass spectrometry (LC/MS) are powerful techniques for the separation of complex mixtures and identification of components, but like all techniques, they need to be used alongside auxiliary techniques to gain a fuller view of SOA chemical composition that includes more fragile or low-volatility species. Recent advancements in ambient ionization methods including direct analysis in real time mass spectrometry (DART-MS) have shown promise in measuring sensitive compounds in complex matrices.
formation was insufficient to counteract POA losses. Equa- tions (1) and (2) fail to predict the complete POA evapora- tion of these experiments. This may be the result of a small fraction of the organic mass being in the condensed phase (May et al., 2012) at the start of photooxidation, which can enhance fractional particle-phase mass loss. Particle mass loss increases rapidly for cases when less than 25 % of the low-volatility organic mass is in the condensed phase. This is the case for experiments with vehicle D4, when ap- proximately 20 % of the total low-volatility organic emis- sions were present in the condensed phase at t = 0 (May et al., 2013a). Vehicle D4 has lower emissions of both low- volatility and volatile organic species, and a lower fraction of organic emissions as POA, than the other gasoline and diesel vehicles considered here. For vehicle D4, assuming that the POA follows a first-order loss rate overestimates the POA concentration, and therefore underestimates SOA formation. The final class of experiments includes one experiment, T63-1 (Fig. 2), where the POA initially evaporates before rising again. The initial POA loss is driven by chemical consumption of POA vapors and a small fraction of low- volatility organic species in the condensed phase, as is the case for experiments with vehicle D4. The subsequent in- crease in POA concentration is a result of the large increase in C OA due to SOA formation.
contribute substantially to SOA in the atmosphere, even if its yield is small (Carlton et al., 2009). There are several isoprene oxidation products that can lead to SOA forma- tion, including isoprene-derived epoxydiols (IEPOX) (Paulot et al., 2009), glyoxal and methyl glyoxal (Fu et al., 2008), gas-phase low-volatility organic compounds (LVOC) pro- duced from gas-phase oxidation of hydroxy hydroperoxides (ISOPOOH) (Krechmer et al., 2015; Liu et al., 2016), and methacryloylperoxynitrate (MPAN) (Surratt et al., 2010). Gas-phase IEPOX, mainly formed from the photooxidation of isoprene under low-NO conditions (Paulot et al., 2009), can efficiently partition onto aqueous acidic aerosols and pro- duce SOA through aqueous-phase reactions (Paulot et al., 2009; Surratt et al., 2010; Gaston et al., 2014a; Zhang et al., 2018). SOA from IEPOX (IEPOX-SOA) is considered at present the dominant isoprene-derived SOA pathway (Marais et al., 2016; Carlton et al., 2018; Mao et al., 2018), compared to a less efficient formation from glyoxal (Knote et al., 2014). Ground-based and aircraft field measurements have shown that IEPOX-SOA can contribute to total OA concentrations by as much as 36 %, especially for forested regions under low NO across the globe (Hu et al., 2015). Several modeling studies have explicitly simulated IEPOX-SOA by consider- ing detailed isoprene gas-phase chemistry and IEPOX uptake (Marais et al., 2016; Budisulistiorini et al., 2017; Stadtler et al., 2018). Figure 1 shows the main chemical pathways of the IEPOX-SOA chemistry in (a) HO 2 - and (b) NO-dominant
parameters were able to represent SOA formation with the same degree of uncertainty as the VBS parameters (i.e., no additional error is introduced by the 2p-VBS fit). It there- fore can be assumed that the SOA yield and mass predictions using the Tsimpidi et al. (2010) VBS parameters and the 2p- VBS parameters produce equivalent results (in the absence of any “aging”), including temperature dependent SOA yields. The 2p-VBS fits result in a reduction from 4 “bins” (8 pa- rameters, typical for VBS) to 2 bins (4 parameters), which can be utilized in existing 2p model frameworks, such as MOZART. The MOZART SOA module does not allow for aging or processing of SOA, thus the gas-phase oxidation (beyond the initial oxidation of the parent VOC) that is of- ten represented in applications of the VBS is not considered in this work. For the precursors included in the MOZART simulations, it was determined that the 2p-VBS parameters represented available chamber data well, with the exception of isoprene. Therefore, the parameters of Henze and Sein- feld (2006) were used. In addition, the MOZART SOA mod- ule includes oxidation of monoterpenes by NO 3 for which
We have shown here that the role of organic oxidation products in new particle formation is particularly impor- tant in determining the indirect radiative effect of biogenic SOA. However, our understanding of which secondary or- ganic species contribute to each stage of new particle forma- tion and growth at different particle sizes is far from complete (e.g. Kulmala et al., 2013). In the absence of a detailed un- derstanding, and bearing in mind computational costs, future model development regarding biogenic SOA could proceed on the basis that at least some (as yet undetermined) frac- tion of the secondaryorganic oxidation products contribute to new particle formation. Our simulations also highlight the important interaction between secondary organics and pri- mary particles from combustion, particularly from fires in the tropics. This suggests that the (simulated) ageing of in- soluble particles should be determined by the availability of condensable material, rather than prescribed (i.e. transferring insoluble particles to the soluble distribution after a fixed pe- riod of time), in order to capture the sensitivity of CCN con- centration to changing levels of secondary organics.
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.
uncertainties in deposition parameters. Including dry and wet deposition of SOA precursors will likely reduce SOA con- centrations. Another limitation to this study is the absence of aqueous SOA formation in aerosols (Ervens, 2015) and cloud water (McNeill et al., 2012). Further laboratory studies are required to provide detailed oxidation mechanisms of VOC species such that they can be implemented into chemistry– climate models. Future modelling work will evaluate dry de- position, wet deposition, and an evolving volatility distribu- tion or SOA precursors, and their impacts on SOA formation. Nevertheless, we have considered SOA formation from a number of different sources in a global composition- climate model, and compared against a consistent set of ob- servations. In doing so, we have highlighted that, overall the inclusion of new sources of SOA improves the ability of the UKCA model to simulate SOA distributions across many world regions. Additionally, the new estimate of the global SOA budget from UKCA lies within the range of estimates from other global modelling studies. Future modelling work should aim to improve confidence in SOA formation mech- anisms, and to explicitly simulate multigenerational oxida- tion products with evolving volatility. Furthermore, observa- tions of SOA are required in regions influenced by biogenic and biomass burning emissions, such as South America and Africa.