Impact of Urban Emissions on a Biogenic Environment during the wet season: Explicit Modeling of the Manaus Plume Organic Chemistry with GECKO-A

The GoAmazon 2014/5 field campaign took place in Manaus (Brazil) and allowed the investigation the interaction between background level biogenic air masses and anthropogenic plumes. We present in this work a box model built to simulate the impact of urban chemistry on biogenic Secondary Organic Aerosol (SOA) formation and composition. An organic chemistry mechanism is generated with the Generator for Chemistry and Kinetics of Organics in the Atmosphere (GECKO-A) to simulate the explicit oxidation of biogenic and anthropogenic compounds. A parameterization is also included to account for 5 the reactive uptake of isoprene oxidation products on aqueous particles. After some reductions of biogenic emissions relative to existing emission inventories, the model is able to reproduce measured primary compounds, ozone and NOx for clean or polluted situations. The explicit model is able to reproduce background case SOA mass concentrations but is underestimating the enhancement observed in the urban plume. Oxidation of biogenic compounds is the major contributor to SOA mass. A Volatility Basis Set parameterization (VBS) applied to the same cases obtains better results than GECKO-A for predicting 10 SOA mass in the box model. The explicit mechanism may be missing SOA formation processes related to the oxidation of monoterpenes that could be implicitly accounted for in the VBS parameterization. 1 https://doi.org/10.5194/acp-2019-1024 Preprint. Discussion started: 21 November 2019 c © Author(s) 2019. CC BY 4.0 License.

on how ethanol blends impact vehicle emissions; the authors should at least acknowledge this literature and comment on how their assumptions about vehicle emissions might impact their results.
Answer 3: We indeed assume that the VOC speciation of traffic emissions in Manaus is similar to that in São Paulo. 35 However our emissions are scaled to the total emissions that were measured in Manaus and thus errors in emissions may come from a different distribution of vehicle types and different fuel blends between the two cities. This has an impact on the nature of emitted VOCs, which in turn would impact the SOA formation potential and oxidants lifetimes in the plume. This is now now acknowledge in the manuscript: Sect. 3.2.2: [. . . ] The difference in the fuel blend used in São Paulo and Manaus can introduce errors in the traffic emissions VOC speciation. For instance, a recent study by  showed that the combustion of fuels with higher ethanol content emits significantly less carbon monoxide and more acetaldehyde. Schifter et al. (2020) showed similar results, and also suggested that ethanol blends emit smaller amounts of simple aromatic compounds (e.g. benzene, toluene). This speciation uncertainty can especially have an impact on oxidants concentrations. Schifter et al. (2020) reported for instance that fuels containing ethanol would potentially produce less ozone after the oxidation of emitted organic species than fuels without ethanol. Moreover, the lifetime of OH is likely to change depending on the speciation of emitted VOCs due to varying reactivities with respect to OH. In the same way that the potential for ozone formation could depend on the use of ethanol fuel blends, it is also possible that the potential for SOA formation would depend on these fuel blends too. [. . . ] Sect. 4.2.4: [. . . ] As a test, we generalized this estimation to all C 10 in the aerosol phase: we replaced each C 10 by the corresponding C 20 and halved its concentration. In this way, we can calculate what would H/C and O/C ratios be in the aerosol phase if aging processes only dimerized C 10 compounds. The resulting modeled van Krevelen diagram is reported on Fig. 10 (labeled w/ dimer.). The impact of C 10 dimerization is relatively strong on O/C ratio, ranging from 0.66 to 0.78 and remaining in the range of measured O/C ratios at T3 site and in the aircraft. H/C ratios are only reduced to 1.88-1.94, still 50% higher than measured H/C at the T3 site and 20% higher than airborne data. [. . . ] As another test, we also estimated what would O/C and H/C ratios be if all C 10 fragmented in the aerosol phase. The resulting modeled van Krevelen diagram is reported on Fig. 10 (labeled w/ frag.). In this case, modeled O/C ratios increase to a range of 0.88 to 0.96 and remain in the higher end of measured ratio at the T3 site. H/C are reduced further than in the dimerization test and sit at the higher end of airborne measured H/C ratios, but they still are 45% higher than H/C ratios measured at the T3 site.
Even if they apparently cannot account for the discrepancy between modeled and measured H/C ratios, the two tests presented here on C 10 compounds in the aerosol phase show the potential importance of adding these missing processes in GECKO-A. These simple tests are however simplifications that overlook important factors in the potential impact on SOA composition: (i) not all C 10 compounds would be affected by these processes, (ii) other compounds than C 10 could react in a similar way, (iii) trimerization, tetramerization and other accretion processes could also occur in the aerosol phase, (iv) missing fragmentation processes could also happen in the gas phase.
Introduction: The Generator for Explicit Chemistry and Kinetics of Organics in the Atmosphere (GECKO-A, Aumont et al., 2005;Camredon et al., 2007) is an excellent tool to model atmospheric organic chemistry with a detailed molecular view.
Comment 17: Line 171. The authors discuss the fact that n-alkanes are perhpas not a good surrogate for diesel fuel and gasoline IVOCs, which are mostly branched and cyclic. However,they do not explain why their estimates are less branched than other work has suggested, and importantly they do not disucss the impacts of these structural differences. They acknowledge 140 that almost none of diesel fuel is comprised of the compounds they are using as surrogates, but do not further discuss this issue. Gentner et al. attempt to put estimates on the impact of branching and rings on SOA, so estimating this uncertainty shouldn't be too difficult.
Answer 17: This IVOC alkane surrogate speciation was first established in Lee-Taylor et al. (2011) to obtain a good volatility distribution, because the identity of individually emitted species was not well known. We kept this simplified IVOCs The vertical range of the experimental data denotes the standard deviation of measured concentrations during events identified as clean (top, blue) and polluted (bottom, orange). The airborne data was measured during plume transects. For each transect, aircraft distance from Manaus was converted to a time separation from Manaus assuming the plume leaves the city at 8am and arrives above T3 at 2pm.
It should be noted that modeled benzene and toluene lines were switched on the original Fig. 5. We fixed that too and updated the text to account for this: Sect. 4.1: The modeled mixing ratio of benzene matches the measurements, between 0.4 and 0.6 ppb, while modeled toluene is closer to the higher range of measurements, between 0.2 and 0.6 ppb during the afternoon. Figure 5 also displays the airborne measurements of the same anthropogenic compounds. The modeled mixing ratios of benzene and toluene decay in a similar way to the concentrations measured during the plume transects. The modeled peak is not seen by the aircraft measurements as the aircraft may not be flying close enough to the emission sources to capture it. Answer 19: We are not aware of measurements at T1 or T2 that could have helped constrain VOCs in the city itself.
However the comparison to airborne measurements mentionned in Comment 18, provides an additional constraint. This two boxes boxmodel used in this study is designed to simulate an air mass traveling over the rainforest. The bottom box is then 165 exposed to fresh Manaus emissions for 1 h, the approximate time it would take for an air mass traveling at 10 m s −1 to cross the urban area. This model design does not require the city chemistry to be at equilibrium before interaction with the box.
After the emission update presented in Comment 8, the two major sources of anthropogenic pollution are accounted for with traffic and power plants emissions. To our knowledge the potential contribution of personal care products VOCs emissinos to anthropogenic emissions has only been evaluated in North America (e.g. Coggon et al., 2018;McDonald et al., 2018;Shah 170 et al., 2020). This contribution is likely to become relatively important in the future with the decrease of vehicle emissions in western developed countries, but is not likely to be important in Manaus in 2014 and 2015.
The interaction of biogenics from the surrounding forest with urban emissions is exactly what happens in the model as soon as it is exposed to Manaus emissions: biogenic emissions are replaced with urban emissions for a short time, but the urban emissions become mixed with the remaining background from biogenic chemistry.

175
Comment 24: Line 362. The authors point out the importance of aging in capturing the polluted SOA,which the GECKO-A model does not really capture. Is this due to a lack of aging in the GECKO-A model? Maybe I missed it, but is the GECKO-A model only oxidizing the gas phase and not aging the particle? Considering the importance of aging on reproducing the SOA mass (Figure 7), could the authors include a parameterization of aging in the GECKO model? This might also address the H/C 200 and O/C issues.
Answer 24: There is no aging parameterization in GECKO-A, oxidation only happens in the gas phase. For technical reasons and lack of resources, we were not able to implement a parameterization of aging in GECKO-A for this paper. In our answer to Comment 10, we estimate the possible impact of dimerization and fragmentation of biogenic condensed species on H/C and O/C but we don't see how to easily estimate the potential impact on SOA mass.

205
Comment 25: Line 367-368. Why is it unclear? It seems to capture polluted periods better than biogenic periods ( Figure   7).
Answer 25: The VBS parameterization is capturing both polluted and biogenic episodes relatively well, but in both situations SOA composition is dominated by biogenic oxidation products. In that sense, we don't know if the same parameterization would apply well in environments where the composition of SOA would be for instance dominated by species of anthropogenic 210 or biomass burning origin.
Comment 26: Line 400-402. The ideas of reducing complexity discussed in this section are very interesting. It's not completely clear to me that some of the conclusions aren't overextended.In particular, the conclusion that "diversity represents the number of species that would be needed to reproduce the same informational content regarding the composition of SOA." The parameter D is based solely on mass fraction, not their physicochemical properties, so it might not capture other properties 215 such as oxygenation or volatility.Imagine a scenario where a small fraction of highly oxygenated compounds drive up O/Cthis might still impact hygroscopicity but would this complexity be captured by D? Or a small fraction of more volatile components might partition between the gas-and particle-phase and drive oxidation of particle mass. While the exact mechanism of implementing such a reduction is out of scope of this mansucript, the overall point is that it's not to me that either of the diversity parameters really capture the complexity. Would it be possible to implement a variant of D for parameters of interest? 220 For instance minimum number of components needed to describe O/C within 10% while capture some fraction of the mass? I'm not sure the best parameters, but the idea would be something that captures some of the properties beyond simply mass.
Answer 26: This is a very interesting take on the reduction issues. We agree with the idea that this diversity approach should be extended to capture other properties than just mass and that we overextended the conclusion about the composition of SOA.
We fixed the sentence you mention as follows: 225 Sect. 4.4: [. . . ] As this number is directly derived from informational entropy, we suggest that the diversity represents the number of species that would be needed to reproduce the same informational content regarding the time evolution of SOA mass in the explicit model. [. . . ] For this paper, we could not explore further this application of information theory to explicit mechanisms reduction. Hopefully this will be the subject of future work. We added the following to the discussion in that same section, raising the issues mentioned in this comment.
Sect. 4.4: [. . . ] Finally, we used in this section an entropy calculation for SOA mass: it is based only on mass fractions of the species composing the modeled organic particles. The effective number of species displayed on Fig. 12 is therefore only meaningful for SOA mass and properties directly linked to it. If the goal is to predict other properties, e.g. hygroscopicity, toxicity or optical properties, assuming we find a way to calculate these with GECKO-A, the diversity defined here would not necessarily be meaningful. For instance, hygroscopicity or toxicity could be driven by a handful of oxygenated species that do not matter for the informational content regarding SOA mass. We did not explore further down this path, as this is not the main subject of this paper, but it may be possible to generalize this definition of informational diversity to properties other than mass.
As an additional note, the idea is not to reduce complexity as written at the beginning of this comment. It is more about evaluating complexity, and showing how many species are needed to effectively produce the same complexity (regarding SOA 230 mass in our example) as the explicit mechanism.
In this work, a box model is run to simulate the evolution of an Amazonian air mass intercepting Manaus emissions during the wet season. Emissions of anthropogenic and biogenic primary VOCs are estimated with available data. The chemical scheme describing the explicit oxidation of these primary compounds is generated with GECKO-A. The resulting detailed simulation is then used to explore the impact of Manaus emissions on the Amazonian biogenic chemistry. Comparisons with aerosol 75 mass spectrometer data and the VBS parameterization are carried out to identify important processes involved in biogenic SOA formation that may not be accounted for in GECKO-A. Finally the potential for reduction of the explicit mechanism is estimated.

Experimental Data
The main instrumented site (referred to as T3 hereafter) of the GoAmazon 2014/5 field campaign was situated 70 km west of 80 Manaus ( Fig. 1). Two aircraft were also deployed, a G-159 Gulfstream I (G-I) (Schmid et al., 2014) that flew at low altitude and mostly sampled the boundary layer and a Gulfstream G550 (HALO) that flew higher altitudes and sampled the free troposphere . The flight tracks are depicted in Martin et al. (2016) and Wendisch et al. (2016). The G-1 airplane mainly flew daytime transects of the Manaus plume between the city and the T3 site.
The detailed instrumentation deployed at T3 and in the airplanes has been described elsewhere . For this 85 study we mainly relied on ground deployed instruments briefly described here.
Ozone concentration measurements made with a Thermo Fisher Model 49i Ozone Analyzer were obtained from the Mobile Aerosol Observing System-Chemistry (MAOS-C).
Due to some issues with the NO x analyzer deployed at T3 by the MAOS-C during the wet season, NO x data reported here is weakly reliable. The values reported here are only qualitative indications of NO x levels in the studied period.

90
OH radicals concentrations were provided by an OH chemical ionization mass spectrometer (Sinha et al., 2008, OH-CIMS).  For the purpose of comparisons with the model, we need to be able to separate time periods representing clean and polluted 95 episodes. Using a fuzzy c-means clustering algorithm (Bezdek, 1981;Bezdek et al., 1984) applied to T3 measurements, de Sá et al. (2018) were able to identify four different clusters corresponding to (i) fresh or (ii) aged (2+ days) biogenic production, and air masses influenced by the (iii) northern or (iv) southern parts of Manaus. Using the timeseries contribution of these clusters, we labeled as background air masses that were identified as being composed of at least 50% of any clean cluster (i or ii). Conversely, air masses that were identified by de Sá et al. (2018) as being composed of at least 50% of any polluted 100 cluster (iii and iv) were labeled as polluted. The clustering methods constrained the classification to only include wet season afternoon air masses that were not exposed to rain in the previous day (see de Sá et al., 2018). These limitations match with our model restrictions which do not include cloud chemistry, nor fire emissions that would be important during the dry season.
For comparison with the model, experimental data were hourly averaged for each cluster.

105
A Lagrangian box model was built to simulate chemistry in the planetary boundary layer and the residual layer for an air parcel traveling over the Amazonian forest and Manaus. Because experimental data compared to the model only contain air masses that were not exposed to rain in the previous day (see Sect. 2 and de Sá et al., 2018), the model simulates biogenic conditions for one day, assuming the air mass was washed out by rain prior to that day. After the one day spinup, biogenic emissions are replaced by urban emissions for one hour during the second day to represent the interaction of the air mass with 110 the Manaus urban area. After the simulated encounter with Manaus, the model inputs return to biogenic emissions until the end of the second day. This simulation is defined hereafter as the "polluted" case. Another simulation is run where the box is only subjected to biogenic emissions for two days, without any exposure to urban emissions to simulate a background case. This simulation is defined hereafter as the "clean" case. This section describes the box model setup, how the emissions were defined and the chemical mechanism used for this study.  Fig. 2). At sunset, stratification is assumed 125 to quickly shrink the PBL to 50 m which results in the contents of the PBL being reallocated to the RL. During the night, the PBL is constrained to linearly grow to reach the next morning level. The PBL height evolution is the same for each of the two simulated days. During the day, the PBL is therefore slowly incorporating residual chemicals resulting from the previous day and night chemistry. Thalman et al. (2017) report PBL heights estimated from ceilometer measurements during the wet season in the central Amazonian Forest, for polluted and background conditions. The measurements reach a maximum of 800 m at 130 around 3pm local time. This value was used to further constrain the PBL height evolution by scaling the SOMCRUS output to reach this measured PBL height maximum. The growth and shrinking of the PBL dilute the expanding box and transfer gases from the shrinking box to the expanding box. This is parameterized according to Eqs. 1 and 2: well-mixed concentrations. The residual layer is also slowly mixed with the free troposphere. The free troposphere is assumed to be a fixed reservoir of CO (80 ppb) and ozone (15 ppb, e.g. Browell et al., 1990;Gregory et al., 1990;Kirchhoff et al., 1990).
Temperature is assumed to follow a sinusoidal daily variation, with an average of 27°C, an amplitude of 4°C and a maximum at 6 pm local time. Relative humidity is initially set at 75% at 6 am (23°C) and is free to evolve with temperature changes 145 assuming water vapor concentration is constant.
Isoprene/10 Monoterpenes   (Martins et al., 2006). Hourly distribution of the traffic emissions is considered to be similar to the hourly traffic distribution in São Paulo (Andrade et al., 2015). In the past decades, Brazil has become known for pioneering the large scale use of ethanol based biofuels. However, due to its isolation and being distant from south Brazilian biofuel producing regions, Manaus traffic doesn't involve consumption of significant amounts of ethanol-based fuel.

GECKO-A 220
All emitted organic compounds were used as inputs to GECKO-A to automatically generate the chemical scheme used in this study. The GECKO-A protocol has been described in detail in Aumont et al. (2005)  With 12 biogenic and 53 anthropogenic precursors ranging from C 2 to C 25 , some reductions are carried out to reduce the size of the generated mechanisms. Species with an estimated vapor pressure below 10 −13 atm are assumed to entirely partition to the aerosol phase so quickly that a description of their gas phase oxidation is not needed (Valorso et al., 2011). Furthermore, lower

Isoprene SOA formation
GECKO-A treats SOA formation through a dynamic approach that converges towards the equilibrium defined by the Pankow formulation of Raoult's Law (Pankow, 1994). However it is likely that isoprene SOA (ISOPSOA) formation is not only controlled by vapor pressure (Paulot et al., 2009). Among factors that have been identified to play a role in ISOPSOA are: aqueous phase oxidation in deliquescent aerosol (e.g. Blando and Turpin, 2000;Ervens et al., 2011;Daumit et al., 2016), organic sul- depends on the acidity of particles, as well as their sulfate and nitrate content. These parameters had to be constrained in the model and were deduced from the T3 AMS measurements and literature data (see Table 1). On the other hand, isoprene oxidation products containing nitrate moieties (dihydroxydinitrates and isoprene nitrate) hydrolyze and form polyols and nitric acid.

265
The sum of all monoterpenes follows a similar increasing trend in the afternoon, from 0.1 to 0.3 ppb. After adjusting biogenic emissions rates (see Sect. 3.2.1), the model is able to reproduce these mixing ratios, with isoprene and monoterpenes being simulated in the higher range :: to ::: the :::::: average : of experimental values. In polluted situations, the model shows a peak of anthropogenic organic compounds when the plume encounters Manaus emissions between 8 and 9 am. This peak reaches 0.3 ppb and 0.2 :: 0.2 :::: ppb ::: and ::: 0.3 : ppb respectively for benzene and toluene (Fig. 5). Their levels decay for the remainder of the day. Because  Figure 6. Experimental (dots, T3 site) and modeled (lines, second day) time evolution of radicals concentrations NOx (left, :::: note ::: log :::: scale), ozone (middle) ::::: mixing ::::: ratios and OH :::::: radicals ::::::::::: concentrations (right, note log scale)mixing ratios. The vertical range of the experimental data denotes the standard deviation of measured concentrations at T3 during events identified as clean (blue) and polluted (orange).
Furthermore, VOCs in the plume are exposed to high OH concentrations, with modeled concentration reaching 1.6 :: 1. as clean and polluted did not exhibit any difference between both situations . (Fig. 6). In that case, there could be issues with the OH measurements at T3. Indirect constraints have shown differences between clean and polluted situations. Liu et al. (2018) derived OH concentrations from isoprene and its oxidation products measurement. They showed that noontime OH concentrations vary between 5×10 5 molec cm −3 in clean situations to 1.5×10 6 molec cm −3 in polluted events. The Shrivastava et al. (2019) 3D model exhibits a similar OH behavior to this work with concentrations at T3 ranging from 2∼5×10 5 molec 300 cm −3 (clean) to more than 4×10 6 molec cm −3 (polluted). The GECKO-A model is therefore likely to be overestimating OH concentrations in the urban plume by a factor of 5 to 10. This could stem from either overestimating NO or underestimating VOCs emissions in the city.
The yields used in this VBS approach were fitted over a variety of low OA loading atmospheric chamber studies of biogenics oxidation under high and low NO x concentrations (Shrivastava et al., 2019). More details about this VBS approach can be 400 found in Shrivastava et al. (2013Shrivastava et al. ( , 2015Shrivastava et al. ( , 2019.

Potential for Reduction of the Explicit GECKO-A mechanism
It is obvious that the chemical mechanisms generated with GECKO-A are too large to be implemented in 3D models. The GECKO-A mechanisms need to be reduced to sizes manageable by 3D models, typically a few hundred species and reactions.
The VBS parameterization used for comparison in this work is fit for low OA loadings, biogenic dominated situations but it is 445 unclear that it should be applied to other situations.
In this section, we are not proposing a much needed new approach to reducing explicit mechanisms with the goal of predicting SOA mass concentrations, but we explore here the potential for reduction of the chemical mechanism that was generated for this study. In other words, what is the theoretical lower limit to the number of species that should be used in a reduced scheme to still be able to model the same SOA mass concentration time profile as the explicit model?
In the clean situation both metrics behave similarly, with a morning increase of the number of species until 10 am, after which the number remains relatively constant until sunset. During daytime, on average 310 :::::::::: N 90% = 292 : species are needed to represent 90% of the SOA mass. The calculated diversity is around 170 ::: 153 effective species. For the polluted situation, 470 the number of species needed to represent 90% of the SOA mass during daytime increases ::::: N 90% :::::::: increases :::::: during ::::::: daytime by about a factor of 7 : 9, reaching about 2300. :::: 2500. : The calculated diversity only increases up to approximately 410 ::: 260 effective species. These increases in the species numbers for the polluted case are logical as the variety of precursors, and hence secondary species that could potentially contribute to SOA, is increased by urban emissions.
The number of species needed to represent most of the modeled SOA mass in all cases seems too high to be used in 3D 475 models applications. Furthermore there is no guarantee that the most important species at a given timestep would be the same most important species at the following timestep. This suggests that reductions should not come from simply selecting species identified as important to represent the variety of species that could arise in the interaction of biogenic air and an urban plume.

Conclusions
An explicit chemical mechanism generated with GECKO-A was used in a box model to simulate a situation similar to the situation studied in Manaus during the GoAmazon 2014/5 field campaign. After scaling down the emissions generated from the MEGAN biogenic emissions model and estimating urban emissions in Manaus, the model was able to reproduce realistic primary organic compounds mixing ratios as well as NO x , ozone and OH concentrations.

500
The model is able to reproduce background SOA mass concentrations but is not able to reproduce the observed enhancement in the polluted plume. When running a Volatility Basis Set approach that was previously applied to the Manaus case (Shrivastava et al., 2019), modeled SOA mass matches measurements which suggests that the incorrect explicit model prediction is not caused by incorrect primary organic compound emissions or oxidant levels. Modeled particle phase organosulfates are within the range of previous measurements  which suggests that isoprene oxidation and SOA formation 505 in the model are reasonably well simulated. In both polluted and clean situations, biogenics are identified as the main contributors to SOA by both GECKO-A and the VBS parameterization. In both approaches, the majority of SOA production is attributed to monoterpenes oxidation and condensation of lower volatility products. Yee et al. (2018) measured and described sesquiterpenes during GoAmazon 2014/5 for the same situations and suggested that these species may be important for modeling studies. However the modeling study of Shrivastava et al. (2019) estimated that the contribution of sesquiterpenes to SOA 510 production is less than 10%. It is more likely that physico-chemical processes involved in monoterpene SOA formation are either unknown or missing in the explicit model. Comparison of modeled and measured elemental ratios (H/C and O/C) indicates that fragmentation of monoterpenes oxidation products and their condensation or reactive uptake to the condensed phase may play an important role in understanding the sources of biogenic SOA mass. This reactive uptake may in turn involve oligomerization and fragmentation processes. :::::::: However, :::::: simple ::::::::: sensitivity :::: tests ::::: show :::: that :::: these ::::::::: processes ::::: alone :::: may ::: not ::::::: explain ::: the experiments, it could implicitly be accounting for these missing processes. Of the high diversity of monoterpenes identified in Amazonia (Jardine et al., 2015), only a handful of monoterpenes have been studied to the extent that we can be as confident in model predictions of SOA formation from monoterpenes as from isoprene. Detailed mechanistic studies of monoterpene oxidation are therefore needed for further incorporation in explicit models to better understand the nature and the magnitude 520 of the contribution of monoterpenes to SOA formation, as well as their response to the interaction with urban pollution (e.g. Claflin and Ziemann, 2018).
Even if a parameterization was implemented in GECKO-A to properly address the formation of isoprene SOA via aqueous phase processes (Marais et al., 2016), to explicitly treat these in a more general way, future GECKO-A developments for mechanism generation will need to include the following: (i) aerosol thermodynamics, for instance via coupling with a model 525 like MOSAIC (Zaveri et al., 2008) or ISORROPIA (Nenes et al., 1998), (ii) aqueous phase processes including explicit dissolution (e.g. Mouchel-Vallon et al., 2013), oxidation (e.g. Mouchel-Vallon et al., 2017), accretion reactions (e.g. Renard et al., 2015), and interaction with dissolved inorganic ions, (iii) explicit treatment of the fate of newly formed species like dimers or organo-sulfates.
One could be tempted to think that since the VBS parameterization is behaving particularly well in this GoAmazon 2014/5 530 case, it could be the answer to predict SOA mass in larger scale 3D models. However this approach is limited by the fact that it was fitted for low biogenic OA loading situations and was run in a limited domain regional model (Shrivastava et al., 2019). One possible way of building reduced mechanisms is to reduce existing detailed chemical mechanisms to sizes manageable by 3D models (e.g. Szopa et al., 2005;Kaduwela et al., 2015). Using an information theory based approach, we provide here a lower limit to the size of these reduced mechanisms, assuming the goal is to produce the same informational content as the explicit 535 mechanism. This lower limit of a few hundred species is four orders of magnitudes lower than the actual number of species that are actually accounted for in the explicit mechanism (4×10 6 ) and shows the potential for progress in future mechanism reduction endeavors. Even if a direct application of this statistical approach to create a reduced mechanism would likely require some atmospheric chemistry breakthrough, it could at least currently be used as a statistical indicator for comparing reduced mechanisms with reference explicit mechanisms. Camredon, M. and Aumont, B.: Assessment of vapor pressure estimation methods for secondary organic aerosol modeling, Atmospheric Jenkin, M. E., Saunders, S. M., and Pilling, M. J.: The tropospheric degradation of volatile organic compounds: A protocol for mechanism Palm, B. B., de Sá, S. S., Day, D. A., Campuzano-Jost, P., Hu, W., Seco, R., Sjostedt, S. J., Park, J.-H., Guenther, A. B., Kim, S., Brito, J.,