A two-dimensional volatility basis set – Part 2: Diagnostics of organic-aerosol evolution

Donahue, Neil M.; Kroll, J. H.; Pandis, Spyros Ν.; Robinson, Allen L.

doi:10.5194/acp-12-615-2012

Cited by 548 publications

(592 citation statements)

References 104 publications

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“…The importance of fragmentation reactions may be lower for cyclic organics such as levoglucosan (for which CÀ ÀC bond cleavage does not split the molecule), which are contained in large amounts in wood-burning OM (Shafizadeh 1985;Rogge et al 1998;Simoneit et al 1999;Fine et al 2001;Graham et al 2002). Nevertheless, fragmentation may play a significant role in the oxidation of combustion aerosols even at atmospherically realistic oxidant levels (Donahue et al 2012).…”

Section: Wood-stove Emission Factorsmentioning

confidence: 99%

“…The amount of SOM formation observed in previous studies has been highly variable, but generally comparable to or greater than POM (Grieshop et al 2009a(Grieshop et al , 2009bHennigan et al 2011;Heringa et al 2011;Ortega et al 2013). Predicting such SOM formation is challenging due to the complex mixture of Volatile Organic Compounds (VOCs) present in the gas phase of combustion aerosols (Donahue et al 2012;Ortega et al 2013). Each constituent of this VOC mixture may condense as SOM if its vapour pressure is lowered by oxidation-initiated reactions.…”

Section: Introductionmentioning

confidence: 99%

“…Each constituent of this VOC mixture may condense as SOM if its vapour pressure is lowered by oxidation-initiated reactions. Mechanistically predicting the production of SOM by aerosol oxidation requires knowledge of the reactivity, vapour pressure, and concentration of the emitted VOCs (May et al 2013) as well as their oxidation products (Donahue et al 2012). Consequently, bulk VOC concentrations cannot be used to effectively predict SOM formation and SOM regulation is better achieved by simulated atmospheric aging.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Organic Emissions from a Wood Stove and a Pellet Stove Before and After Simulated Atmospheric Aging

Corbin

Keller

Lohmann

et al. 2015

Aerosol Science and Technology

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Logwood and pellet stoves are popular heating sources around the world. The particulate matter emitted from such stoves contains organic particulate matter (OM), soot, and ash, each of which may have significant effects on climate and health. In this study, the primary OM (POM) emitted from a wood stove and a pellet stove operated according to standard Swiss testing protocols were characterized using aerosol mass spectrometry. The POM mass spectra were found to be highly reproducible, and contained CO C as the dominant ion. Because the POM emitted by such stoves is typically enhanced by the condensation of gaseous organics following atmospheric aging, the secondary OM (SOM) formation potential of these stoves was simulated using the Micro Smog Chamber (MSC) designed by Keller and Burtscher in 2012. In general, OM emission factors from MSCaged aerosols were comparable to lower-time-resolution results from the literature, although the MSC exposed aerosols to much higher concentrations of oxidants and therefore produced OM that was more oxidized than expected for atmospheric samples. In addition, the logwood-stove particles remained highly aspherical even after oxidation, indicating that mixing with an external aerosol is required for these particles to become spherical. The one exception to this observation occurred when the wood failed to ignite and appeared to generate tar-ball OM particles.

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Section: Wood-stove Emission Factorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Organic Emissions from a Wood Stove and a Pellet Stove Before and After Simulated Atmospheric Aging

Corbin

Keller

Lohmann

et al. 2015

Aerosol Science and Technology

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“…1B is built on the 2D volatility basis set (2D-VBS) framework. The 2D-VBS classifies organics based on volatility (C Ã ) and oxidation extent (1,(28)(29)(30). To construct a lumped mechanism, described in detail in the SI Text, we discretize this 2D space into bins and consider the changes to C Ã and O∶C caused by OH oxidation.…”

Section: Modelmentioning

confidence: 99%

Aging of biogenic secondary organic aerosol via gas-phase OH radical reactions

Donahue

Henry

Mentel

et al. 2012

Proc. Natl. Acad. Sci. U.S.A.

269

303

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The Multiple Chamber Aerosol Chemical Aging Study (MUCHA-CHAS) tested the hypothesis that hydroxyl radical (OH) aging significantly increases the concentration of first-generation biogenic secondary organic aerosol (SOA). OH is the dominant atmospheric oxidant, and MUCHACHAS employed environmental chambers of very different designs, using multiple OH sources to explore a range of chemical conditions and potential sources of systematic error. We isolated the effect of OH aging, confirming our hypothesis while observing corresponding changes in SOA properties. The mass increases are consistent with an existing gap between global SOA sources and those predicted in models, and can be described by a mechanism suitable for implementation in those models.atmospheric chemistry | biosphere-atmosphere interactions O rganic aerosol (OA) comprises a large fraction of fine-particle mass (PM 2.5 ) (1). In the developed world, 1-2% of deaths are blamed on inhalation of PM 2.5 (2), and the leading uncertainty in climate forcing is the interplay between the number of fine particles large enough to nucleate cloud droplets and the amount of sunlight reflected by those clouds (3). Oxidation and condensation of organics play a major but uncertain role in both phenomena.Traditional models treat most OA as nonvolatile primary OA (POA), augmented by secondary OA (SOA) (4), and they underpredict OA concentrations by a factor of 3-10 (5). α-Pinene is a major biogenic SOA source, sometimes used to represent all SOA in global models (4, 6). However, less than 20% of the carbon from fresh α-pinene oxidation condenses in chambers at room temperature; (7) the remainder is gaseous (Fig. 1A). This "chamber" SOA is modestly oxidized, with an oxygen to carbon ratio ðO∶CÞ < 0.4 (7). It is unambiguously semivolatile: Yields rise with increasing SOA mass loading (8, 9) and decreasing temperature (10), and the SOA evaporates upon heating (11-13) and after isothermal dilution (14).In contrast, ambient OA is highly oxidized (0.5 ≤ O∶C ≤ 1.0) (1, 15) and not very volatile (16). Ambient SOA is much less volatile than ambient POA (16). Consequently, "chamber" SOA does not represent the atmosphere. Our hypothesis is that homogeneous gas-phase aging by OH is a major missing process connecting chamber studies to the atmosphere. Considerable attention has been paid to heterogeneous uptake of oxidants to particles (17, 18), and recently gas-phase oxidation of semivolatile primary emissions (19), but the degree to which gas-phase oxidation can age chamber SOA is uncertain (1,4,18,20).OA resides in the atmosphere for about one week (21), while the gas-phase lifetimes of major semivolatile SOA constituents are far shorter. Typical α-pinene products pinonaldehyde, cispinonic acid, and pinic acid all have lifetimes of only a few hours for summertime conditions (22). Without question, oxidation of semivolatile SOA vapors will perturb the equilibrium phase partitioning of these constituents. Because almost all of the first-generation products are less volatile than α...

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“…However, these mechanisms track thousands to millions of chemical species and are computationally impractical for modeling multi-generational oxidation in 3-D models. Recently, there has been the development of two frameworks of intermediate complexity that allow for the treatment of multi-generational oxidation (and other aerosol processes) during SOA formation: the two-dimensional volatility basis set (2D-VBS) that uses vapor pressure and the O : C (oxygen to carbon) ratio as the independent variables Donahue et al, 2012a) and the statistical oxidation model (SOM) that uses the number of carbon atoms and oxygen atoms per molecule as independent variables (Cappa and Wilson, 2012). Both have provisions to treat fragmentation of the reactants as a function of their oxygen content and can be parameterized from chamber measurements (Cappa et al, 2013;Zhao et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Multi-generational oxidation model to simulate secondary organic aerosol in a 3-D air quality model

et al. 2015

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Abstract. Multi-generational gas-phase oxidation of organic vapors can influence the abundance, composition and properties of secondary organic aerosol (SOA). Only recently have SOA models been developed that explicitly represent multigenerational SOA formation. In this work, we integrated the statistical oxidation model (SOM) into SAPRC-11 to simulate the multi-generational oxidation and gas/particle partitioning of SOA in the regional UCD/CIT (University of California, Davis/California Institute of Technology) air quality model. In the SOM, evolution of organic vapors by reaction with the hydroxyl radical is defined by (1) the number of oxygen atoms added per reaction, (2) the decrease in volatility upon addition of an oxygen atom and (3) the probability that a given reaction leads to fragmentation of the organic molecule. These SOM parameter values were fit to laboratory smog chamber data for each precursor/compound class. SOM was installed in the UCD/CIT model, which simulated air quality over 2-week periods in the South Coast Air Basin of California and the eastern United States. For the regions and episodes tested, the two-product SOA model and SOM produce similar SOA concentrations but a modestly different SOA chemical composition. Predictions of the oxygen-tocarbon ratio qualitatively agree with those measured globally using aerosol mass spectrometers. Overall, the implementation of the SOM in a 3-D model provides a comprehensive framework to simulate the atmospheric evolution of organic aerosol.

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A two-dimensional volatility basis set – Part 2: Diagnostics of organic-aerosol evolution

Cited by 548 publications

References 104 publications

Organic Emissions from a Wood Stove and a Pellet Stove Before and After Simulated Atmospheric Aging

Organic Emissions from a Wood Stove and a Pellet Stove Before and After Simulated Atmospheric Aging

Aging of biogenic secondary organic aerosol via gas-phase OH radical reactions

Multi-generational oxidation model to simulate secondary organic aerosol in a 3-D air quality model

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