Simulation of the evolution of biomass burning organic aerosol 1 with different volatility basis set schemes in PMCAMx-SRv 1 . 0 2 3

13 A source-resolved three-dimensional chemical transport model, PMCAMx-SR, was 14 applied in the continental U.S. to investigate the contribution of the various components (primary 15 and secondary) of biomass burning organic aerosol (bbOA) to organic aerosol levels. Two 16 different schemes based on the volatility basis set were used for the simulation of the bbOA 17 during different seasons. The first is the default scheme of PMCAMx-SR and the second is a 18 recently developed scheme based on laboratory experiments of the bbOA evolution. 19 The simulations with the alternative bbOA scheme predict much higher total bbOA 20 concentrations when compared with the base case ones. This is mainly due to the high emissions 21 of intermediate volatility organic compounds (IVOCs) assumed in the alternative scheme. The 22 oxidation of these compounds is predicted to be a significant source of secondary organic 23 aerosol. The impact of the other parameters that differ in the two schemes is low to negligible. 24 The monthly average maximum predicted concentrations of the alternative bbOA scheme were 25 approximately an order of magnitude higher than those of the default scheme during all seasons. 26 The performance of the two schemes was evaluated against observed total organic 27 aerosol concentrations from several measurement sites across the US. The results were 28 mixeddifferent for the different seasons examined. The default scheme performed better during 29


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Over the past decades, atmospheric aerosols, also known as particulate matter (PM), are 36 at the forefront of atmospheric chemistry research due to their adverse impacts on human health, 37 climate change, and visibility. More specifically, fine particulate matter with an aerodynamic 38 diameter less than 2.5 μm (PM2.5) is associated with decreased lung function (Gauderman et al.,39 2000), bronchitis incidents (Dockery et al., 1996), respiratory diseases (Pope, 1991;Schwartz et 40 al., 1996;Wang et al., 2008) and eventually increases in mortality (Dockery et al., 1993). PM2.5 41 also affects the planet's energy balance (Schwartz et al., 1996), and causes visibility reduction in 42 urban centers but also rural areas (Seinfeld and Pandis, 2006). 43 One of the most important components of fine PM almost everywhere is organic aerosol 44 (OA) (Andreae and Crutzen, 1997; Roberts et al., 2001;Kanakidou et al., 2005). Despite its 45 importance, OA remains poorly understood due to its physicochemical complexity (Goldstein 46 and Galbally, 2007). OA is traditionally separated into primary (POA), which is emitted directly 47 into the atmosphere as particles, and secondary OA (SOA), which is OA that is formed from 48 gaseous precursors that after oxidation and condensation form organic particulate matter 49 (Seinfeld and Pandis, 2006). SOA includes components produced during the oxidation of semi-   (Bond et al., 2004). In this work, the term biomass burning includes 60 3 wildfires in forests and other areas, prescribed burning which is a small wildfire set intentionally 61 (Tian et al., 2008;Chiodi et al., 2018) in order to decrease the likelihood of major wildfires, 62 agricultural waste burning, and residential burning. 63 The simulation of bbOA has been the topic of numerous studies all of them concluding 64 that it is an important source of fine particles (Tian et al., 2009). Most of them assumed that 65 bbOA is non-volatile and inert (Chung and Seinfeld, 2002    concluded that the modified parameterization improved the model performance for total OA as 91 well as the OA components especially during the winter.

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The aim of the current study is to implement the alternative VBS scheme proposed by 93 Ciarelli et al. (2017a, b)  new parameterization that has improved bbOA predictions in Europe will close the gap between 104 predictions and observations in the US too.

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In most modelling studies so far biomass burning OA (bbOA) is grouped with the rest of 106 the primary and secondary OA components and is simulated in exactly the same way. In this

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In the current study we will study in detail the impact of the different partitioning          bbOA was a factor of 5 lower ( Figure 6). The differences between the two schemes exceeded 10 336 μg m -3 on a monthly average basis over California, and were above 1 μg m -3 over a large part of 337 the western US ( Figure S1).

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During September there were major wild fires once more in California but also in Oregon   PMCAMx-SR simulation can also be found in Table S1 for completeness.

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The bbOA predictions of the alternative scheme are in general higher than those of the 395 base scheme. This leads to a small improvement of the performance of PMCAMx-SR during 396 April especially for the low bbOA threshold ( Table 2) with bbOA above all three thresholds ( Table 2). The bias increases for the areas closer to the 408 fires (higher threshold). These results strongly suggest that the alternative scheme is too 409 aggressive in the production of SOA-iv during this summertime period with intensive wild fires.

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The changes in the predictions of the model were small, a few percent or less ( Figure S2).   PMCAMx-SR simulation can also be found in Table S1 for completeness.

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The bbOA predictions of the alternative scheme are in general higher than those of the 525 base scheme. This leads to a small improvement of the performance of PMCAMx-SR during 526 April especially for the low bbOA threshold ( for the medium threshold, while for the high threshold the fractional bias increases (from -0.14 to 530 0.28) while the fractional error decreases (from 0.53 to 0.5). So overall, the use of the alternative 531 scheme appears to lead to a small improvement of the PMCAMx-SR predictions during this 532 period, but with a tendency towards overprediction especially close to the sources of biomass 533 burning.

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During July, the base scheme reproduced the OA observations in areas affected by 535 biomass burning with little bias. The alternative scheme predicts a significantly higher SOA-iv 536 production during this period and results in a substantial overprediction of the OA levels in areas 537 with bbOA above all three thresholds ( Table 2). The bias increases for the areas closer to the 538 fires (higher threshold). These results strongly suggest that the alternative scheme is too 539 aggressive in the production of SOA-iv during this summertime period with intensive wild fires. in stoves). In this study we test its applicability for the simulation of the bbOA from other 553 sources (wildfires, prescribed and agricultural burning) in different periods.

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The alternative scheme predicts in general much higher bbOA levels than the baseline 555 scheme for all seasons. Both schemes suggest that secondary production is a major process for including the assumed enthalpies of vaporization and molecular weights was small.

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The performance of PMCAMx-SR using the two schemes was evaluated against  The alternative scheme considered here has been derived based on experiments using 577 residential heating emissions. An assumption used in most biomass burning OA simulation 578 20 efforts so far is that the same parameterization can be used for the different burning types: 579 wildfires, agricultural burning, residential heating, prescribed burning, etc. Our work provides 580 some support to the hypothesis that different parameterizations may be needed for residential 581 heating and wildfires. This is clearly an issue that deserves additional attention in future 582 modeling efforts. contributed to the writing of the paper.

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Competing interests. The authors declare that they have no conflict of interest.