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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Atmospheric Aqueous Aerosol Interfaces: Thermodynamic Modeling and Biphasic Microfluidic Flows with Fluid-Fluid Interfaces

Atmospheric Aqueous Aerosol Interfaces: Thermodynamic Modeling and Biphasic Microfluidic Flows with Fluid-Fluid Interfaces

Figure 1.4: Microfluidic devices design for interfacial tensiometry. 6, atmospherically relevant liquid-liquid interfaces are studied using the aforemen- tioned techniques. In particular, aqueous phases are studied containing secondary organic aerosol (SOA), an important and abundant class of particulate matter that are formed through photochemical reactions. [44–46] Characterization of SOA forma- tion processes and equilibrium is poorly characterized and must be studied further to better understand atmospheric aerosol’s role in climate and human health. [44, 47] Results are discussed in these chapters for interfacial tensions of SOA particle mimics as well as their implications for particle morphology. [39] Chapter 5 is a study of solute mixtures containing methylglyoxal and formaldehyde, plus an inorganic electrolyte, ammonium sulfate. Aqueous phases with these specific water soluble compounds have been shown to evolve chemically through self-reactions. [48] Chapter 6 includes results for aqueous dicarboxylic acid solutions as well as chamber samples of SOA material extracted in methanol. For the organic acids, both their binary aqueous solutions and their ternary solutions with ammonium sulfate are studied.
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Glyoxal processing by aerosol multiphase chemistry: towards a kinetic modeling framework of secondary organic aerosol formation in aqueous particles

Glyoxal processing by aerosol multiphase chemistry: towards a kinetic modeling framework of secondary organic aerosol formation in aqueous particles

Henry’s law constants), further chemical processing in the aqueous phase might lead to different products if nonvolatile species are formed. Thermodynamic equilibrium might be only reached upon the kinetic time scale of the uptake and if aqueous phase loss rates are slow enough to compensate con- tinuous transport from the gas phase. These species accumu- late in the aqueous phase and build up higher concentrations more quickly in the small particle volumes. Each oxidation step includes the formation of an intermediate peroxy radi- cal (RO 2 , R I O 2 , R II O 2 ). These radicals either decay to the oxidation product and HO 2 (von Sonntag and Schuchmann, 1991) or recombine with HO 2 or RO 2 . While the decay leads to oxidation products (R I , R II ), the accumulation of peroxy radicals in a smaller water volume favors the recombination in aqueous particles. The recombination of any organic per- oxy radical leads to an increase in carbon atoms in the prod- ucts with oligomer-like structures.
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Chamber Studies and Modeling of Secondary Organic Aerosol Formation

Chamber Studies and Modeling of Secondary Organic Aerosol Formation

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.
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Modeling secondary organic aerosol formation from biogenic hydrocarbons

Modeling secondary organic aerosol formation from biogenic hydrocarbons

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.
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Modeling Regional Secondary Organic Aerosol Using the Master Chemical Mechanism

Modeling Regional Secondary Organic Aerosol Using the Master Chemical Mechanism

Surface reactive uptake a b s t r a c t A modified near-explicit Master Chemical Mechanism (MCM, version 3.2) with 5727 species and 16,930 reactions and an equilibrium partitioning module was incorporated into the Community Air Quality Model (CMAQ) to predict the regional concentrations of secondary organic aerosol (SOA) from volatile organic compounds (VOCs) in the eastern United States (US). In addition to the semi-volatile SOA from equilibrium partitioning, reactive surface uptake processes were used to simulate SOA formation due to isoprene epoxydiol, glyoxal and methylglyoxal. The CMAQ-MCM-SOA model was applied to simulate SOA formation during a two-week episode from August 28 to September 7, 2006. The southeastern US has the highest SOA, with a maximum episode-averaged concentration of ~12 m g m 3 . Primary organic aerosol (POA) and SOA concentrations predicted by CMAQ-MCM-SOA agree well with AMS-derived hydrocar- bon-like organic aerosol (HOA) and oxygenated organic aerosol (OOA) urban concentrations at the Moody Tower at the University of Houston. Predicted molecular properties of SOA (O/C, H/C, N/C and OM/ OC ratios) at the site are similar to those reported in other urban areas, and O/C values agree with measured O/C at the same site. Isoprene epoxydiol is predicted to be the largest contributor to total SOA concentration in the southeast US, followed by methylglyoxal and glyoxal. The semi-volatile SOA com- ponents are dominated by products from b -caryophyllene oxidation, but the major species and their concentrations are sensitive to errors in saturation vapor pressure estimation. A uniform decrease of saturation vapor pressure by a factor of 100 for all condensable compounds can lead to a 150% increase in total SOA. A sensitivity simulation with UNIFAC-calculated activity coefficients (ignoring phase separa- tion and water molecule partitioning into the organic phase) led to a 10% change in the predicted semi- volatile SOA concentrations.
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Modeling of Organic Aerosol during the MEGAPOLI Summer Campaign in the Paris Region

Modeling of Organic Aerosol during the MEGAPOLI Summer Campaign in the Paris Region

 The “Volatility Basis Set” VBS approach has been implemented into a regional CTM (CHIMERE) and evaluated with measurements from the MEGAPOLI Paris summer campaign. Urban / suburban s[r]

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Thermodynamic and Aerosol Controls in Southeast Pacific Stratocumulus

Thermodynamic and Aerosol Controls in Southeast Pacific Stratocumulus

To more completely characterize the statistical distri- bution of the model reflectivity, we calculate contoured frequency by altitude diagrams (CFADs; Yuter and Houze 1995) for the four simulations (Fig. 15). The CFADs in- clude the entire range of simulated reflectivity from 240 to 50 dBZ, ranging well below the sensitivity of the RHB ra- dar. CFADs from all four cases are superficially quite sim- ilar. The consistency of the modal reflectivity (;210 dBZ in all the cases) is consistent with the similarity in the liquid water content and other thermodynamic profiles in Fig. 11. The increase with height of the modal reflectivity indicated by the dashed line on the Deep simulation panel corresponds to the increase in liquid water content (mostly via droplet size) with height in updrafts. Because many of these updraft structures are reminiscent of trade cumulus updrafts, we anticipate that their liquid water content values are substantially subadiabatic. The reflectivity mode associated with the precipitation core is evident below 0.6 km. For a given drop spectrum, smaller drops are preferentially evaporated as they fall and large drops continue to coagulate, which serves to explain the in- creases in modal reflectivity as precipitation falls toward the surface, even though the total precipitation decreases. The most striking difference between the simulations relates to the instances of large values of reflectivity in the distribution tail at each altitude. The reflectivity corre- sponding to the smallest contour value (10 25 ) gives a rough indication of the frequency of occurrence of large reflectivities in each simulation. The largest reflectivity values are slightly less frequent in the Doubled CCN simulation but notably reduced in the two shallow cases. F IG . 14. Cumulative and normalized frequency distribution (PDF) of surface precipitation rate, calculated over the
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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

Note that the higher masses (m/z>165) are dominated by noise. tions derived from our estimated sensitivities is equal to the reacted isoprene, our estimated sensitivity must represent a lower limit for the actual sensitivity of the CIMS technique to these compounds. It is noted that the m/z 393 ion is formed with a relatively slower rate than all other product ions. A small amount of aerosol is observed. At t =15:40, a second pulse of N 2 O 5 (∼50 ppb) is introduced into the chamber and the remaining 82 ppb isoprene is completely consumed. As seen from Fig. 6, the concentrations of all intermediate prod- ucts increase accordingly and more aerosol is produced. The last pulse of N 2 O 5 ( ∼ 210 ppb) is added at t =19:00. Since all isoprene has been consumed, the additional NO 3 radicals react mainly with the first-generation products, as indicated by the decay of m/z 230, 232, and 248, 185, 377, and 393 ions. Of all of the observed products, it appears that m/z 232 and 377 ions are the most reactive with NO 3 radicals, and their decays in excess NO 3 are strongly correlated with aerosol growth. The rest of the product ions display rela- tively slower decay kinetics. The decay of the major prod- uct ion at m/z 230 does not appear to correlate with aerosol growth, as the concentration of the m/z 230 ion continues to decrease throughout the experiment but there is no fur- ther aerosol growth. Since the CIMS instrument has only 0.5 AMU resolution and it cannot distinguish products of similar or identical molecular weight, it is likely that many of ob- served ions comprise isomers formed from the NO 3 attack at different positions. The fact that many of the observed prod- uct ions show two distinct decay time scales indicates that these isomers have substantially different reactivity towards NO 3 radicals.
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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

Statistical measures including the mean bias (MB), corre- lation coefficient (Corr), normalized mean bias (NMB) and normalized mean error (NME) (Yu et al., 2006) are used to evaluate the simulations against observational data. Obser- vational data are available for organic carbon (OC) and to- tal carbon (TC) from the Speciated Trends Network (STN) and the Interagency Monitoring for Protected Visual Envi- ronments (IMPROVE). While both OC and TC from IM- PROVE are used for model evaluation, only TC data from STN are used, as STN uses the thermo-optical transmittance protocol for OC that is different from the one used by IM- PROVE (Zhang et al., 2012). In addition, the measurements for STN OC are not blank corrected for carbon on the back- ground filter (Wang and Zhang, 2012). The OA / OC ratios vary across locations in the continental US (CONUS) de- pending on whether the OA is dominated by secondary for- mation (Aitken et al., 2008) or it contains more aliphatic hy- drocarbons (Turpin and Lim, 2001). In this study, two ratios, 1.4 and 2.1, are used to convert simulated OA to OC based on a number of studies in the literature (Turpin and Lim, 2001; Aitken et al., 2008; Xu et al., 2015). As the simula- tions are based on CONUS with varying OA properties (less or more oxidized OA), the use of two OA / OC ratios can represent the different types of OA present for all locations in the US. Spatial plots, time series plots at specific sites, and overlay plots are used to evaluate model performance. The IMPROVE sites chosen for the time series plots include the visibility-protected areas in Brigantine National Wildlife Refuge (NWR), NJ, Death Valley National Park (NP), CA, Swanqwarter National Wildlife Refuge (NWR), NC, and the Tallgrass Prairie National Preserve, KS. The Brigantine NWR is a tidal wetland and has a shallow bay, the Death Valley NP is a desert, and the Swanqwarter NWR is a coastal brackish marsh. The time series plots are made at four STN sites including two urban sites (in Washington, DC, and Boise, ID), one industrial site (in Tampa, FL), and one ru- ral/agricultural site (in Liberty, KS). SOA, hydroxyl radi- cal (OH), and hydroperoxy radical (HO 2 ) data are also avail-
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Modeling organic aerosols in a megacity: potential contribution of semi-volatile and intermediate volatility primary organic compounds to secondary organic aerosol formation

Modeling organic aerosols in a megacity: potential contribution of semi-volatile and intermediate volatility primary organic compounds to secondary organic aerosol formation

Previous studies of SI-SOA in Mexico City have focused on SOA production within the Mexico City basin during a short period of time (Dzepina et al., 2009; Tsimpidi et al., 2010) (15 h and 4 days, respectively). Dzepina et al. (2009) also performed a 3-day box-model aging simulation that sug- gested that SI-SOA could increase substantially in the out- flow from Mexico City. However the spatially and tempo- rally limited framework of the previous studies did not al- low investigating the significance of SI-SOA over regional scales and the multi-day processing of S/IVOC in the Mex- ico City plume in a 3-D context. Modeling results sug- gest strongly that the transport of OA from the regional scale is a substantial contributor to the observed SOA lev- els within Mexico City. Tsimpidi et al. (2010) assumed this SOA background to be between 3 and 7 µg/m 3 at the bound- aries of their ∼150×150 km 2 model domain to achieve a better agreement with observations. Hodzic et al. (2009) showed that the regional background is partially due to bio- genic SOA (∼1.5 µg/m 3 ) advected from the coastal mountain ranges. Regional POA emissions associated with surround- ing populated areas (e.g. Cuernavaca, Puebla, Toluca) and biomass burning are also important and can contribute sub- stantial amounts of S/IVOC vapors that, as they age, parti- tion to the aerosol. Hodzic et al. (2009) reported that the V-SOA formed from biomass burning emissions with a tradi- tional SOA module was very small. However it is likely that S-SOA from biomass burning may be the dominant source of SOA from these emissions (Grieshop et al., 2009) and may also contribute to the regional background. These unan- swered questions provide motivation for reevaluating the sig- nificance of this formation process in a regional context over a two week time period.
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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

4.1 Abstract Chemical mechanisms play an important role in simulating the atmospheric chemistry of volatile organic compound oxidation. Comparison of mechanism simulations with laboratory chamber data tests our level of understanding of the prevailing chemistry as well as the dynamic processes occurring in the chamber itself. α-Pinene photooxidation is a well-studied system experimentally, for which detailed chemical mechanisms have been formulated. Here, we present the results of simulating low- NO α-pinene photooxidation experiments conducted in the Caltech chamber with the Generator for Explicit Chemistry and Kinetics of Organics in the Atmosphere (GECKO-A) under varying concen- trations of seed particles and OH levels. Unexpectedly, experiments conducted at low and high OH levels yield the same secondary organic aerosol (SOA) growth, whereas GECKO-A predicts greater SOA growth under high OH levels. SOA formation in the chamber is a result of a competition among the rates of gas-phase oxidation to low-volatility products, wall deposition of these products, and condensation into the aerosol phase. Various processes – such as photolysis of condensed-phase products, particle-phase dimerization, and peroxy radical autoxidation – are explored to rationalize the observations. In order to explain the observed similar SOA growth at different OH levels, we conclude that vapor wall loss in the Caltech chamber is likely of order 10 –5 s –1 , consistent with previous experimental measurements in that chamber. We find that GECKO-A tends to overpredict the contribution to SOA of later-generation oxidation products under high-OH conditions. More- over, we propose that autoxidation may alternatively resolve some or all of the measurement–model discrepancy, but this hypothesis cannot be confirmed until more explicit mechanisms are established for α-pinene autoxidation. The key role of the interplay among oxidation rate, product volatility, and vapor–wall deposition in chamber experiments is illustrated.
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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.

As shown in Figure A7, underpredictions of SO 2 concentrations occur at the SEARCH and AQS sites and overpredictions occur at the CASTNET sites, with higher NMEs against AQS and SEARCH than CASTNET. This may indicate possible underestimates in SO 2 emissions or too much conversions of SO 2 to SO 4 2- at the SEARCH and AQS sites but overestimate in SO 2 emissions and or too little conversions of SO 2 to SO 4 2- at the CASTNET sites. The differences may also arise based on different measurement methods for different networks. For CASTNET, SO 2 and HNO 3 in g m -3 are collected on a sampler on a weekly basis, while for AQS and SEARCH, their mixing ratios are measured in ppb hourly using a continuous ambient air monitoring sampler. These results seem to be contrary to results found in literature. For example, Yu et al. (2010) and Sarwar et al. (2014) showed that current models tend to under-predict SO 4 2- and over-predict SO 2 compared to observations. A reason for the underprediction of SO 2 compared to SO 4 2- could be the fact that in this study, heterogeneous chemistry was included with a gamma value of 3.0 x 10 -5 , which could have resulted in a tendency for the partitioning to shift towards the aerosol phase. NO 2 mixing ratios are generally overpredicted in all seasons except for JFD against AQS with a slight underprediction. The trends between their NMBs and NMEs at both AQS and SEARCH sites for all seasons are relatively consistent. Column NO 2 against the SCIAMACHY NO 2 shown in Wang et al.
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A new aerosol flow reactor to study secondary organic aerosol

A new aerosol flow reactor to study secondary organic aerosol

SOA was collected using both an ELPI and onto precondi- tioned 47 mm quartz fibre filters. The reactor was used as a tool to generate large quantities of SOA mass. Subsequently, no corrections have been made for gaseous- or particulate- phase wall losses in this work. The ELPI was used in all the experiments shown in Table 1. The use of the SIFT-MS, NO x and O 3 analysers depended on their availability with other projects, although they were primarily used in the α- pinene experiments. The calibration of these instruments is discussed in the Supplement. Quartz fibre filters were pre- conditioned in a furnace at 550 ◦ C for 5 h to remove any organic compounds before use. The ELPI collects particles with a size range of 0.006 to 10 µm onto size-specific foil- lined impactor plates. The SOA collected from the ELPI in each experiment was transferred from all the foil-lined im- pactor plates (non-size-specific) into two clean glass vials and weighed. One vial was kept at the University of York for compositional analysis and the second vial shipped in dry ice ( − 80 ◦ C) to the University of Bristol for single-particle measurements. The impactor foils were replaced and the im- pactor was cleaned with methanol and water prior to each experiment. All SOA samples were wrapped in foil to mini- mize photolysis degradation and stored in a freezer at − 20 ◦ C until analysis.
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Chemistry of Secondary Organic Aerosol Formation

Chemistry of Secondary Organic Aerosol Formation

I am grateful for the professors that served on my candidacy and thesis commit- tees. This includes John Seinfeld, my advisor, Paul Wennberg, Rick Flagan, and Michael Homann (candidacy) and Mitchio Okumura (thesis). I thank my advisor, John Seinfeld, for inspiring me to pursue graduate studies in atmospheric chemistry. I saw for the rst time the seminal gure on the uncertain role of aerosols on radiative forcing from the IPCC 2007 report in John's seminar at UC Riverside during my senior year of college. I remember leaving that seminar charged with the motivation to better understand aerosol chemistry, and it served as motivation in my successful research proposal for a National Science Foundation Graduate Research Fellowship that supported me while at Caltech. Since being at Caltech, I have appreciated John's advising, as I recall key moments when he took the initiative to address many of my concerns before I had to state them. He has shaped the way I look at and approach scientic problems, and I have always valued his professional mentorship.
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Impact of Aerosol-Cloud Cycling on Aqueous Secondary Organic Aerosol Formation

Impact of Aerosol-Cloud Cycling on Aqueous Secondary Organic Aerosol Formation

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
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Aerosol mass spectrometer constraint on the global secondary organic aerosol budget

Aerosol mass spectrometer constraint on the global secondary organic aerosol budget

Zhang, Q., Jimenez, J. L., Canagaratna, M. R., Allan, J. D., Coe, H., Ulbrich, I., Alfarra, M. R., Takami, A., Middlebrook, A. M., Sun, Y. L., Dzepina, K., Dunlea, E., Docherty, K., De- 10 Carlo, P. F., Salcedo, D., Onasch, T., Jayne, J. T., Miyoshi, T., Shimono, A., Hatakeyama, S., Takegawa, N., Kondo, Y., Schneider, J., Drewnick, F., Borrmann, S., Weimer, S., Demerjian, K., Williams, P., Bower, K., Bahreini, R., Cottrell, L., Griffin, R. J., Rautiainen, J., Sun, J. Y., Zhang, Y. M., and Worsnop, D. R.: Ubiquity and dominance of oxygenated species in organic aerosols in anthropogenically-influenced Northern Hemisphere midlatitudes, Geophys. Res. 15
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Aerosol mass spectrometer constraint on the global secondary organic aerosol budget

Aerosol mass spectrometer constraint on the global secondary organic aerosol budget

uncertain. Recent top-down estimates, based either on the mass balance of volatile organic carbon (VOC) or on scaling of the sulfate budget, suggest a global source ranging from 120–1820 Tg (SOA) a − 1 (Goldstein and Galbally, 2007, Hal- lquist et al., 2009) 1 . Meanwhile atmospheric models typ- ically use bottom-up estimates which combine emission in- ventories for VOCs with laboratory based SOA yields to give a global SOA formation of 12–70 Tg (SOA) a − 1 (Kanakidou et al., 2005). The current uncertainty in the global SOA source (12–1820 Tg (SOA) a − 1 ) is therefore very substantial. In addition to the total budget of SOA being highly uncer- tain, the relative contributions from anthropogenic, biogenic, and biomass burning sources are also poorly constrained. Regional and global atmospheric models using “traditional” SOA parameterizations (those developed until 2006) formed SOA mostly from biogenic VOCs and typically showed large SOA underestimations in polluted regions (e.g. Heald et al., 2005; Volkamer et al., 2006; Hodzic et al., 2010a) but not for clean biogenic regions (Tunved et al., 2006; Chen et al., 2009; Hodzic et al., 2009; Slowik et al., 2010). Recently, for- mation of SOA from additional sources has been included in models. Several box (Dzepina et al., 2009), regional (Hodzic et al., 2010a; Tsimpidi et al., 2010) and global (Pye and Sein- feld, 2010) modelling studies have explored enhanced forma- tion of SOA from semivolatile and intermediate volatility or- ganic compounds (S/IVOC), which are SOA precursors emit- ted by anthropogenic and biomass burning sources (Robin- son et al., 2007). These studies reported that such precur- sors may be an important and previously neglected regional and global SOA source. However, there is still substantial uncertainty in the concentrations, reaction rates, and SOA yields of S/IVOC precursors. Some new parameterizations of SOA formation from anthropogenic VOCs (Lane et al., 2008; Tsimpidi et al., 2010) result in very high mass yields (e.g.
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On the implications of aerosol liquid water and phase separation for organic aerosol mass.

On the implications of aerosol liquid water and phase separation for organic aerosol mass.

of organic water. Similar to the results for the CTR site (Sect. 3.2.3), daytime concentrations of SOC increased but were still low compared to observations. In general, the variability in the bias increased as a result of water interactions, while the mean bias decreased. Some caution should be applied when comparing model predictions and observations. Measurements of total aerosol mass from IMPROVE and CSN networks are made under relative humidities of 30–50%, and quartz filters for OC analysis from IMPROVE may be subject to ambient conditions in the field and during shipping before analysis (Solomon et al., 2014). Exposure to low RH could cause evaporation of reversible aqueous SOA (El- Sayed et al., 2016). Kim et al. (2015) have reported that the IMPROVE measurements of OC were 27% lower than colocated SEARCH measurements during the summer of 2013 and hypothesized the difference to be due to evaporation from the IMPROVE filters during and after sampling. Episodic field campaign observations may be subject to sampling biases as well. Dryers are used ahead of many online aerosol chemistry instruments, and most aerosol water is expected to evaporate in an aerodynamic lens inlet used on many instruments (Zelenyuk et al., 2006; Matthew et al., 2008). Such drying can cause changes in the aerosol phase state (Pajunoja et al., 2016) and could potentially lead to changes in partitioning of soluble organic compounds. El-Sayed et al. (2016) have reported a loss of WSOC after drying. Those authors used a post-drying residence time of 7 s, which is much longer than the post-drying time used by the AMS in SOAS (approximately 1 s) and the time in the aerodynamic lens (approximately 0.01 s). A prior study reported that evaporation of ammonium nitrate, a water-soluble and semivolatile species, was not observed when using post-drying residence times of about 1 s (Guo et al., 2016). While this topic should be subject to additional research, the AMS data in SOAS are unlikely to have significant biases due to this effect.
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Thermodynamic modeling of interstitials in titanium alloys

Thermodynamic modeling of interstitials in titanium alloys

Introduction Chapter 1 underlying phenomenon. Computational methods using a combination of first-principles and statistical mechanics methods can be employed to model the thermodynamics of oxidation. Similar methods have previously been used to model the Zr-O system[28]. Free energy at finite temperature for stable and metastable phases in the system can be derived in order to determine the oxidation sequence after annealing the sample. Additionally, the relative strain between different oxide phases which cause the formation of cracks and voids in the sample can be estimated. The cracks provide access to oxygen for regions far from the surface of the sample thereby changing the oxidation sequence when compared to equilibrium. Modeling the phase stability of the system with vacancies and other point defects can be used to analyze the formation of porosity across the cross section.
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