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

Pye, Havala O. T.; Murphy, Benjamin N.; Xu, Lu; Ng, N. L.; Carlton, Annmarie G.; Guo, Hongyu; Weber, R. J.; Vasilakos, Petros; Appel, K. Wyat; Budisulistiorini, Sri Hapsari; Surratt, Jason D.; Nenes, Athanasios; Hu, Weiwei; Jimenez, José L.; Isaacman-VanWertz, Gabriel; Misztal, Pawel K.; Goldstein, Allen H.

doi:10.5194/acp-17-343-2017

Cited by 209 publications

(238 citation statements)

References 135 publications

(212 reference statements)

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“…The H : C is calculated from O : C using insights from Heald et al (2010) -a common assumption for deriving molecular weights of VBS species in chemical transport models. The species enthalpy of vaporization is assumed to be a linear function of C * (May et al, 2013b;Epstein et al, 2010), and the Henry's law coefficient is prescribed at 2 × 10 8 M atm −1 , which is in the range reported for oxidized organic vapors by Hodzic et al (2014) and Pye et al (2017).…”

Section: Poa Semivolatile Partitioning and Agingmentioning

confidence: 99%

“…CMAQv5.2 also includes aqueous processing of isoprene epoxides, glyoxal, and methylglyoxal, as well as production of oligomeric species from particlephase reaction of traditional SOA compounds. We do not employ the recent liquid-liquid phase partitioning algorithms implemented by Pye et al (2017).…”

Section: Soa From Traditional Precursorsmentioning

confidence: 99%

“…The WRF core model version 3.6.1 was applied for meteorological inputs, and boundary conditions were obtained from a 36 km by 36 km CMAQ simulation using boundary conditions informed by GEOS-Chem (Henderson et al, 2014). As explained by Pye et al (2015) and Pye et al (2017), anthropogenic emissions were based on the Environmental Protection Agency (EPA) National Emissions Inventory 2011 v1, and biogenic emissions were obtained from BEIS v3.6.1. This implementation included enhanced detail in the formation of isoprene SOA and organic nitrate kinetics and partitioning.…”

Section: Model Configuration and Analysismentioning

confidence: 99%

“…To achieve this end, atmospheric models routinely rely on estimates of volatility (i.e., vapor pressure or saturation concentration) and solubility in a predominantly aqueous phase. Although research efforts demonstrate clearly the importance of organic solubility in water on phase partitioning and particle viscosity Hodas et al, 2015;Pajunoja et al, 2015;Riipinen et al, 2015;Wania et al, 2015;Isaacman-VanWertz et al, 2016;Jathar et al, 2016b;Pye et al, 2017;Shiraiwa et al, 2017), improvements to the conceptual model of organic compounds partitioning into an organic-rich particulate phase alone are useful before the entire system is treated holistically.…”

Section: Introductionmentioning

confidence: 99%

“…The magnitude of this bias likely varies substantially among experimental systems (i.e., precursor and oxidant identity) and experimental conditions (e.g., seed concentration, relative humidity, temperature; Krechmer et al, 2016;Nah et al, 2016). Finally, aerosol liquid water may absorb soluble organic vapors and shift the partitioning to enhance OA concentrations as explored by Pye et al (2017).…”

Section: Introductionmentioning

confidence: 99%

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Semivolatile POA and parameterized total combustion SOA in CMAQv5.2: impacts on source strength and partitioning

et al. 2017

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Abstract. Mounting evidence from field and laboratory observations coupled with atmospheric model analyses shows that primary combustion emissions of organic compounds dynamically partition between the vapor and particulate phases, especially as near-source emissions dilute and cool to ambient conditions. The most recent version of the Community Multiscale Air Quality model version 5.2 (CMAQv5.2) accounts for the semivolatile partitioning and gas-phase aging of these primary organic aerosol (POA) compounds consistent with experimentally derived parameterizations. We also include a new surrogate species, potential secondary organic aerosol from combustion emissions (pcSOA), which provides a representation of the secondary organic aerosol (SOA) from anthropogenic combustion sources that could be missing from current chemical transport model predictions. The reasons for this missing mass likely include the following: (1) unspeciated semivolatile and intermediate volatility organic compound (SVOC and IVOC, respectively) emissions missing from current inventories, (2) multigenerational aging of organic vapor products from known SOA precursors (e.g., toluene, alkanes), (3) underestimation of SOA yields due to vapor wall losses in smog chamber experiments, and (4) reversible organic compounds-water interactions and/or aqueous-phase processing of known organic vapor emissions. CMAQ predicts the spatially averaged contribution of pcSOA to OA surface concentrations in the continental United States to be 38.6 and 23.6 % in the 2011 winter and summer, respectively.Whereas many past modeling studies focused on a particular measurement campaign, season, location, or model configuration, we endeavor to evaluate the model and important uncertain parameters with a comprehensive set of United States-based model runs using multiple horizontal scales (4 and 12 km), gas-phase chemical mechanisms, July, 2011). Model improvements manifest better correlations (e.g., the correlation coefficient at Pasadena at night increases from 0.38 to 0.62) and reductions in underprediction during the photochemically active afternoon period (e.g., bias at Pasadena from −5.62 to −2.42 µg m −3 ). Daily averaged predictions of observations at routine-monitoring networks from simulations over the continental US (CONUS) in 2011 show modest improvement during winter, with mean biases reducing from 1.14 to 0.73 µg m −3 , but less change in the summer when the decreases from POA evaporation were similar to the magnitude of added SOA mass. Because the model-performance improvement realized by including the relatively simple pcSOA approach is similar to that of more-complicated parameterizations of OA formation and aging, we recommend caution when applying these morecomplicated approaches as they currently rely on numerous uncertain parameters.The pcSOA parameters optimized for performance at the southern and northern California sites lead to higher OA formation than is observed in the CONUS evaluation. This may be due to any of the following: variations in ...

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