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

Abstract

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...

Sources of organic aerosols in Europe: a modeling study using CAMx with modified volatility basis set scheme

“…The winter OOA is still underestimated at most sites. It could come from the missing pathways of SOA formation such as the aqueous processing of water-soluble organics (Ervens et al, 2011), which was found to contribute up to ∼ 20 % of winter OA measured in Bologna (Gilardoni et al, 2016;Meroni et al, 2017). The uncertainties in the model parameters (reaction rate, yield, etc.)…”

Influence of semi- and intermediate-volatile organic compounds (S/IVOC) parameterizations, volatility distributions and aging schemes on organic aerosol modelling in winter conditions

“…Total OA is the sum of POA and SOA. The overall configuration of the modelling chain follows the one presented in Meroni et al (2017). CAMx v6.40 was applied over two nested domains, the outer covering the whole Italian peninsula with a spatial resolution of 15km and the inner one covering the Po Valley area and small parts of other countries at 5km resolution ( Figure 1).…”

“…Research and Forecasting model (WRF) (Skamarock, et al, 2008), applied over three nested domains, the largest of which covering Europe and Northern Africa at 45km resolution and the two innermost covering Italy and the Po Valley area at 15km and 5km resolution, respectively. Hourly anthropogenic emission fields were computed by the Sparse Matrix Operator for Kernel Emission model (SMOKE v3.5), which processes inventory data from three different levels (regional, Italian and European data) as in Meroni et al (2017). Total hourly emission fields were obtained by adding SMOKE fields to biogenic and sea salt emissions, estimated using the Model of Emissions of Gases and Aerosols from Nature (MEGAN v2.03) (Guenther, et al, 2006) and SEASALT model (Gong, 2003), respectively.…”

“…However, the large complexity of OA chemical composition, with thousands of organic chemical species found in the ambient aerosol (Goldstein & Galbally, 2007), as well as the complex atmospheric processing of organic compounds strongly limited scientific progress in the OA modelling area (Hallquist, et al, 2009;Fuzzi, et al, 2015). Within the atmospheric modelling community, there is mounting evidence thatdespite an overall good agreement in gaseous pollutants -OA mass is in most applications underestimated mainly because of the not well reproduced secondary (SOA) fraction (Meroni, et al, 2017;Ciarelli, et al, 2016;Woody, et al, 2016;Zhang, et al, 2013;Bergström, et al, 2012;Hodzic, et al, 2010). The traditional scheme for OA modelling in Chemical Transport Models (CTMs) is based on the so called "Two-product approach" by Odum et al (1996).…”

“…Details about theoretical aspects of VBS framework are provided in Donahue et al (2006); Donahue et al (2011); Donahue et al (2012b). Several applications of the VBS scheme to CTMs in different case studies can be found in the recent scientific literature (Fountoukis, et al, 2011;Tsimpidi, et al, 2011;Bergström, et al, 2012;Zhang, et al, 2013;Fountoukis, et al, 2014;Koo, et al, 2014;Ciarelli, et al, 2016;Woody, et al, 2016;Fountoukis, et al, 2016;Meroni, et al, 2017). The general conclusion stemming from these works is that the VBS scheme enhances the prediction of both OA levels and degree of oxidation, although the high number of parameters to be constrained in the VBS scheme causes a large uncertainty in the models results.…”