1.5-Dimensional volatility basis set approach for modeling organic aerosol in CAMx and CMAQ

Koo, Bonyoung; Knipping, Eladio; Yarwood, Greg

doi:10.1016/j.atmosenv.2014.06.031

Cited by 154 publications

(201 citation statements)

References 49 publications

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“…Secondary inorganic compounds evolution is 141 described by thermodynamic algorithm ISORROPIA (Nenes et al, 1998). According to the main goal of the 142 study, OA is separately simulated adopting both the algorithms available in CAMx: SOAP (Strader et al, 143 1999) and 1.5D-VBS (Koo et al, 2014). 144 The SOAP approach considers VOC gas-phase oxidation chemistry, forming condensable gases (CG), and 145 equilibrium partitioning between condensable gas and secondary organic aerosol (SOA) for a number of 146 CG/SOA pairs.…”

Section: Camx Configuration and Modelled Domain 138mentioning

confidence: 99%

“…The 2-dimension 78 approach (2D-VBS) describes OA evolution in the 2-D space defined by effective saturation concentration 79 C* (μg m -3 ) and the oxidation degree (Donahue et al, 2012). We use a hybrid VBS approach 1.5D-VBS 80 (Koo et al, 2014), where OA evolution follows specific path in such space, reducing computational cost. The 81 implementation of VBS approach in CTMs introduced a valuable improvement both in model performance 82 as well as in the knowledge of the key processes influencing the modeled results.…”

Section: Introductionmentioning

confidence: 99%

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Investigating the role of chemical and physical processes on organic aerosol modelling with CAMx in the Po Valley during a winter episode

Meroni

Pirovano

Gilardoni

et al. 2017

Atmospheric Environment

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13Traditional aerosol mechanisms underestimate the observed organic aerosol concentration, especially due to 14 the lack of information on secondary organic aerosol (SOA) formation and processing. In contrast VBS slightly underestimates the contribution from fossil-fuel combustion (HOA), indicating that 31 POA emissions related to road transport are either underestimated or associated to higher volatility classes. 32The VBS scheme under-predictes the SOA too, but to a lesser extent than CAMx-SOAP. SOA 33 underestimation can be related to corresponding underestimation of either aging processes or precursor 34emissions. This indicates that improvements in the emission inventories for semi-and intermediate-volatility 35 organic compounds are needed for further progress in this area. Finally, the comparison between modeled 36 and observed SOA sources points out the urgency to include processing of OA in particle water phase into 37 SOA formation mechanisms, to reconcile model results and observations. 38 Highlights 39• CAMx performance for OA are worse than for other PM components 40• SOAP scheme shows a better performance than VBS, due to an error compensation 41• VBS allows a better repartition of primary and secondary OA than SOAP scheme 42• POA volatility distribution and SVOC and IVOC emissions need improvement 43• Aqueous phase mechanism is necessary to reconcile OA observations and modeling 44 45 CTMs implementing standard SOA chemistry often over-predict fresh POA and underpredict SOA, 74 especially in summer (Bergstrom et al., 2012). 75 KeywordsThe volatility basis set (VBS) approach Donahue et al. (2006Donahue et al. ( , 2011 allows to take into account POA 76 volatility and multiple generation SOA production. VBS can be used in two configurations. The 1-dimension 77 approach (1D-VBS) describes OA evolution based on OA volatility (Donahue et al., 2006). The 2-dimension 78 approach (2D-VBS) describes OA evolution in the 2-D space defined by effective saturation concentration 79 C* (μg m -3 ) and the oxidation degree (Donahue et al., 2012). We use a hybrid VBS approach 1.5D-VBS 80 (Koo et al., 2014), where OA evolution follows specific path in such space, reducing computational cost. The 81 implementation of VBS approach in CTMs introduced a valuable improvement both in model performance 82 as well as in the knowledge of the key processes influencing the modeled results. For example Zhang et al. 83 (2013) showed that in a simulation with non-volatile POA and a simplified SOA formation mechanism POA 84 are largely overestimated, while SOA are underestimated. The application of the VBS scheme indicated that 85 also the volatility distribution of the aerosols is extremely important (Lane et al., 2008; Fountoukis et al., 86 2011). Namely, the distribution of OA emissions into the low volatility bins appears to be important for the 87 predicted POA because it has great impact on the initial partitioning between the aerosol and the gas phase 88 (Tsimpidi et al., 2010). Bergstrom et al. (2012) showed...

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Section: Camx Configuration and Modelled Domain 138mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Investigating the role of chemical and physical processes on organic aerosol modelling with CAMx in the Po Valley during a winter episode

Meroni

Pirovano

Gilardoni

et al. 2017

Atmospheric Environment

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“…The CAMx version 5.41 with VBS scheme (ENVIRON, 2011;Koo et al, 2014) was used in this study to simulate an EMEP measurement campaign between 25 February and 26 March 2009 in Europe. The modelling method and input data were the same as those used in the EURODELTA III (ED III) project, described in detail in Ciarelli et al (2016).…”

Section: Regional Modelling With Camxmentioning

confidence: 99%

Modelling winter organic aerosol at the European scale with CAMx: evaluation and source apportionment with a VBS parameterization based on novel wood burning smog chamber experiments

et al. 2017

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Abstract. We evaluated a modified VBS (volatility basis set) scheme to treat biomass-burning-like organic aerosol (BBOA) implemented in CAMx (Comprehensive Air Quality Model with extensions). The updated scheme was parameterized with novel wood combustion smog chamber experiments using a hybrid VBS framework which accounts for a mixture of wood burning organic aerosol precursors and their further functionalization and fragmentation in the atmosphere. The new scheme was evaluated for one of the winter EMEP intensive campaigns (February-March 2009) against aerosol mass spectrometer (AMS) measurements performed at 11 sites in Europe. We found a considerable improvement for the modelled organic aerosol (OA) mass compared to our previous model application with the mean fractional bias (MFB) reduced from −61 to −29 %.We performed model-based source apportionment studies and compared results against positive matrix factorization (PMF) analysis performed on OA AMS data. Both model and observations suggest that OA was mainly of secondary origin at almost all sites. Modelled secondary organic aerosol (SOA) contributions to total OA varied from 32 to 88 % (with an average contribution of 62 %) and absolute concentrations were generally under-predicted. Modelled primary hydrocarbon-like organic aerosol (HOA) and primary biomass-burning-like aerosol (BBPOA) fractions contributed to a lesser extent (HOA from 3 to 30 %, and BBPOA from 1 to 39 %) with average contributions of 13 and 25 %, respectively. Modelled BBPOA fractions were found to represent 12 to 64 % of the total residential-heating-related OA, with increasing contributions at stations located in the northern part of the domain.Source apportionment studies were performed to assess the contribution of residential and non-residential combustion precursors to the total SOA. Non-residential combustion and road transportation sector contributed about 30-40 % to SOA formation (with increasing contributions at urban and near industrialized sites), whereas residential combustion (mainly related to wood burning) contributed to a larger extent, around 60-70 %. Contributions to OA from residential combustion precursors in different volatility ranges werePublished by Copernicus Publications on behalf of the European Geosciences Union. 7654 G. Ciarelli et al.: Modelling winter organic aerosol at the European scale with CAMx also assessed: our results indicate that residential combustion gas-phase precursors in the semivolatile range (SVOC) contributed from 6 to 30 %, with higher contributions predicted at stations located in the southern part of the domain. On the other hand, the oxidation products of higher-volatility precursors (the sum of intermediate-volatility compounds (IVOCs) and volatile organic compounds (VOCs)) contribute from 15 to 38 % with no specific gradient among the stations.Although the new parameterization leads to a better agreement between model results and observations, it still underpredicts the SOA fraction, suggesting that uncertainties in the new scheme ...

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“…3370 A. P. : an efficient module to compute volatility and oxygen content approach, semi-volatile primary emissions, chemical aging, and SOA formation were unified within a common framework that is ideally suited for regional and global chemical modeling. Since 2006, many regional (Lane et al, 2008;Murphy and Pandis, 2009;Tsimpidi et al, , 2011Ahmadov et al, 2012;Athanasopoulou et al, 2013;Koo et al, 2014;Fountoukis et al, 2014;Ciarelli et al, 2017;Gao et al, 2017) and global (Pye and Seinfeld, 2010;Jathar et al, 2011;Jo et al, 2013;Tsimpidi et al, 2014;Hodzic et al, 2016) modeling studies have used the VBS to account for the semi-volatile nature and chemical aging of organic compounds, demonstrating improvements in reproducing the OA budget and its chemical resolution.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, the 2-D VBS approach has been mostly adopted in box and 1-D Lagrangian models (Murphy et al, 2011(Murphy et al, , 2012Chacon-Madrid et al, 2013;Zhao et al, 2015;Paciga et al, 2016). Koo et al (2014) introduced a hybrid VBS approach for use in three-dimensional chemical transport models (CTMs) that combines the simplicity of the VBS with the ability to track the evolution of OA in the 2-D space of volatility and oxygen content.…”

Section: Introductionmentioning

confidence: 99%

ORACLE 2-D (v2.0): an efficient module to compute the volatility and oxygen content of organic aerosol with a global chemistry–climate model

et al. 2018

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Abstract. A new module, ORACLE 2-D, simulating organic aerosol formation and evolution in the atmosphere has been developed and evaluated. The module calculates the concentrations of surrogate organic species in two-dimensional space defined by volatility and oxygen-to-carbon ratio. It is implemented into the EMAC global chemistry-climate model, and a comprehensive evaluation of its performance is conducted using an aerosol mass spectrometer (AMS) factor analysis dataset derived from almost all major field campaigns that took place globally during the period 2001-2010. ORACLE 2-D uses a simple photochemical aging scheme that efficiently simulates the net effects of fragmentation and functionalization of the organic compounds. The module predicts not only the mass concentration of organic aerosol (OA) components, but also their oxidation state (in terms of O : C), which allows for their classification into primary OA (POA, chemically unprocessed), fresh secondary OA (SOA, low oxygen content), and aged SOA (highly oxygenated). The explicit simulation of chemical OA conversion from freshly emitted compounds to a highly oxygenated state during photochemical aging enables the tracking of hygroscopicity changes in OA that result from these reactions. OR-ACLE 2-D can thus compute the ability of OA particles to act as cloud condensation nuclei and serves as a tool to quantify the climatic impact of OA.

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1.5-Dimensional volatility basis set approach for modeling organic aerosol in CAMx and CMAQ

Cited by 154 publications

References 49 publications

Investigating the role of chemical and physical processes on organic aerosol modelling with CAMx in the Po Valley during a winter episode

Investigating the role of chemical and physical processes on organic aerosol modelling with CAMx in the Po Valley during a winter episode

Modelling winter organic aerosol at the European scale with CAMx: evaluation and source apportionment with a VBS parameterization based on novel wood burning smog chamber experiments

ORACLE 2-D (v2.0): an efficient module to compute the volatility and oxygen content of organic aerosol with a global chemistry–climate model

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