Comment on acp-2022-463

Abstract. Increasing evidence from experimental studies suggests that the losses of semi-volatile vapors to chamber walls could be responsible for the underestimation of organic aerosol (OA) in air quality models that use parameters obtained from chamber experiments. In this study, a box model with a volatility basis set (VBS) scheme was developed, and the secondary organic aerosol (SOA) yields with vapor wall loss correction were optimized by a genetic algorithm based on advanced chamber experimental data for biomass burning. The vapor wall loss correction increases the SOA yields by a factor of 1.9–4.9 and leads to better agreement with measured OA for 14 chamber experiments under different temperatures and emission loads. To investigate the influence of vapor wall loss correction on regional OA simulations, the optimized parameterizations (SOA yields, emissions of intermediate-volatility organic compounds from biomass burning, and enthalpy of vaporization) were implemented in the regional air quality model CAMx (Comprehensive Air Quality Model with extensions). The model results from the VBS schemes with standard (VBS_BASE) and vapor-wall-loss-corrected parameters (VBS_WLS), as well as the traditional two-product approach, were compared and evaluated by OA measurements from five Aerodyne aerosol chemical speciation monitor (ACSM) or aerosol mass spectrometer (AMS) stations in the winter of 2011. An additional reference scenario, VBS_noWLS, was also developed using the same parameterization as VBS_WLS except for the SOA yields, which were optimized by assuming there is no vapor wall loss. The VBS_WLS generally shows the best performance for predicting OA among all OA schemes and reduces the mean fractional bias from −72.9 % (VBS_BASE) to −1.6 % for the winter OA. In Europe, the VBS_WLS produces the highest domain average OA in winter (2.3 µg m−3), which is 106.6 % and 26.2 % higher than VBS_BASE and VBS_noWLS, respectively. Compared to VBS_noWLS, VBS_WLS leads to an increase in SOA by up to ∼80 % (in the Balkans). VBS_WLS also leads to better agreement between the modeled SOA fraction in OA (fSOA) and the estimated values in the literature. The substantial influence of vapor wall loss correction on modeled OA in Europe highlights the importance of further improvements in parameterizations based on laboratory studies for a wider range of chamber conditions and field observations with higher spatial and temporal coverage.

Abstract. A parameterization for secondary organic aerosol (SOA) production based on the volatility basis set (VBS) approach has been coupled with microphysics and radiative schemes in the Weather Research and Forecasting model with Chemistry (WRF-Chem) model. The new chemistry option called "RACM-MADE-VBS-AQCHEM" was evaluated on a cloud resolving scale against ground-based and aircraft measurements collected during the IMPACT-EUCAARI (Intensive Cloud Aerosol Measurement Campaign – European Integrated project on Aerosol Cloud Climate and Air quality interaction) campaign, and complemented with satellite data from MODIS. The day-to-day variability and the diurnal cycle of ozone (O3) and nitrogen oxides (NOx) at the surface are captured by the model. Surface aerosol mass concentrations of sulfate (SO4), nitrate (NO3), ammonium (NH4), and organic matter (OM) are simulated with correlations larger than 0.55. WRF-Chem captures the vertical profile of the aerosol mass concentration in both the planetary boundary layer (PBL) and free troposphere (FT) as a function of the synoptic condition, but the model does not capture the full range of the measured concentrations. Predicted OM concentration is at the lower end of the observed mass concentrations. The bias may be attributable to the missing aqueous chemistry processes of organic compounds and to uncertainties in meteorological fields. A key role could be played by assumptions on the VBS approach such as the SOA formation pathways, oxidation rate, and dry deposition velocity of organic condensable vapours. Another source of error in simulating SOA is the uncertainties in the anthropogenic emissions of primary organic carbon. Aerosol particle number concentration (condensation nuclei, CN) is overestimated by a factor of 1.4 and 1.7 within the PBL and FT, respectively. Model bias is most likely attributable to the uncertainties of primary particle emissions (mostly in the PBL) and to the nucleation rate. Simulated cloud condensation nuclei (CCN) are also overestimated, but the bias is more contained with respect to that of CN. The CCN efficiency, which is a characterization of the ability of aerosol particles to nucleate cloud droplets, is underestimated by a factor of 1.5 and 3.8 in the PBL and FT, respectively. The comparison with MODIS data shows that the model overestimates the aerosol optical thickness (AOT). The domain averages (for 1 day) are 0.38 ± 0.12 and 0.42 ± 0.10 for MODIS and WRF-Chem data, respectively. The droplet effective radius (Re) in liquid-phase clouds is underestimated by a factor of 1.5; the cloud liquid water path (LWP) is overestimated by a factor of 1.1–1.6. The consequence is the overestimation of average liquid cloud optical thickness (COT) from a few percent up to 42 %. The predicted cloud water path (CWP) in all phases displays a bias in the range +41–80 %, whereas the bias of COT is about 15 %. In sensitivity tests where we excluded SOA, the skills of the model in reproducing the observed patterns and average values of the microphysical and optical properties of liquid and all phase clouds decreases. Moreover, the run without SOA (NOSOA) shows convective clouds with an enhanced content of liquid and frozen hydrometers, and stronger updrafts and downdrafts. Considering that the previous version of WRF-Chem coupled with a modal aerosol module predicted very low SOA content (secondary organic aerosol model (SORGAM) mechanism) the new proposed option may lead to a better characterization of aerosol–cloud feedbacks.

The formation and evolution of secondary organic aerosol during haze events in Beijing in wintertime

A new secondary organic aerosol (SOA) parameterization based on the volatility basis set is implemented in a regional air quality model WRF‐CHEM. Full meteorological and chemistry simulations are carried out for the United States for August–September 2006. Predicted organic aerosol (OA) concentrations are compared against surface measurements made by several networks and aircraft data from the TexAQS‐2006 field campaign. Elemental carbon simulations are also evaluated in order to evaluate the model's ability to capture their emissions, transport, and removal. Certain measurement limitations, such as daily averaged OA concentrations, impose some difficulties on the model evaluation, and hourly averaged OA measurements provide more informative constraints compared to daily concentrations. The updated model demonstrates a significant improvement in simulating the OA concentrations compared to the standard WRF‐CHEM, which predicts very little SOA. The improvement in organic carbon (OC) predictions is noticeable in correlations and model bias. The correlations of OC exceed that of the persistence forecasts for hourly concentrations in the southeast United States during daytime. The updated traditional SOA yields still lead to an underestimation of observed OA, while addition of the multigenerational volatile organic compound (VOC) oxidation drastically improves model performance. However, several key uncertainties remain in SOA formation and loss mechanisms, which are characterized through several perturbation simulations. Dry deposition of VOC oxidation products is an important factor in the atmospheric SOA budget. The combination of the biogenic VOC emissions, updated SOA yields, and aging mechanism result in biogenic SOA being the dominant OA component for much of the nonurban United States.

Abstract. Secondary pollutants such as ozone, secondary inorganic aerosol, and secondary organic aerosol formed in the plumes of megacities can affect regional air quality. In the framework of the FP7/EU MEGAPOLI (Megacities: Emissions, urban, regional and Global Atmospheric POLlution and climate effects, and Integrated tools for assessment and mitigation) project, an intensive campaign was launched in the greater Paris region in July 2009. The major objective was to quantify different sources of organic aerosol (OA) within a megacity and in its plume. In this study, we use airborne measurements aboard the French ATR-42 aircraft to evaluate the regional chemistry-transport model CHIMERE within and downwind of the Paris region. Two mechanisms of secondary OA (SOA) formation are used, both including SOA formation from oxidation and chemical aging of primary semivolatile and intermediate volatility organic compounds (SI-SOA) in the volatility basis set (VBS) framework. As for SOA formed from traditional VOC (volatile organic compound) precursors (traditional SOA), one applies chemical aging in the VBS framework adopting different SOA yields for high- and low-NOx environments, while another applies a single-step oxidation scheme without chemical aging. Two emission inventories are used for discussion of emission uncertainties. The slopes of the airborne OA levels versus Ox (i.e., O3 + NO2) show SOA formation normalized with respect to photochemical activity and are used for specific evaluation of the OA scheme in the model. The simulated slopes were overestimated slightly by factors of 1.1, 1.7 and 1.3 with respect to those observed for the three airborne measurements, when the most realistic "high-NOx" yields for traditional SOA formation in the VBS scheme are used in the model. In addition, these slopes are relatively stable from one day to another, which suggests that they are characteristic for the given megacity plume environment. The configuration with increased primary organic aerosol (POA) emissions and with a single-step oxidation scheme of traditional SOA also agrees with the OA / Ox slopes (about ± 50 % with respect to the observed ones); however, it underestimates the background. Both configurations are coherent with observed OA plume buildup, but they show very different SI-SOA and traditional anthropogenic SOA (ASOA) contributions. It is hence concluded that available theoretical knowledge and available data in this study are not sufficient to discern the relative contributions of different types of anthropogenic SOA in the Paris pollution plume, while its sum is correctly simulated. Based on these simulations, for specific plumes, the anthropogenic OA buildup can reach between 8 and 10μg m−3. For the average of the month of July 2009, maximum OA increases due to emissions from the Paris agglomeration are noticed close to the agglomeration at various length scales: several tens (for primary OA) to hundreds (for SI-SOA and ASOA) of kilometers from the Paris agglomeration. In addition, BSOA (SOA formed from biogenic VOC precursors) is an important contributor to regional OA levels (inside and outside the Paris plume).

Modeling of secondary organic aerosols (SOA) based on two commonly used air quality models in China: Consistent S/IVOCs contribution but large differences in SOA aging

Abstract. Air quality and climate influence each other through the uncertain processes of aerosol formation and cloud droplet activation. In this study, both processes are improved in the Weather, Research and Forecasting model with Chemistry (WRF/Chem) version 3.7.1. The existing Volatility Basis Set (VBS) treatments for organic aerosol (OA) formation in WRF/Chem are improved by considering the following: the secondary OA (SOA) formation from semi-volatile primary organic aerosol (POA), a semi-empirical formulation for the enthalpy of vaporization of SOA, and functionalization and fragmentation reactions for multiple generations of products from the oxidation of VOCs. Over the continental US, 2-month-long simulations (May to June 2010) are conducted and results are evaluated against surface and aircraft observations during the Nexus of Air Quality and Climate Change (CalNex) campaign. Among all the configurations considered, the best performance is found for the simulation with the 2005 Carbon Bond mechanism (CB05) and the VBS SOA module with semivolatile POA treatment, 25 % fragmentation, and the emissions of semi-volatile and intermediate volatile organic compounds being 3 times the original POA emissions. Among the three gas-phase mechanisms (CB05, CB6, and SAPRC07) used, CB05 gives the best performance for surface ozone and PM2. 5 concentrations. Differences in SOA predictions are larger for the simulations with different VBS treatments (e.g., nonvolatile POA versus semivolatile POA) compared to the simulations with different gas-phase mechanisms. Compared to the simulation with CB05 and the default SOA module, the simulations with the VBS treatment improve cloud droplet number concentration (CDNC) predictions (normalized mean biases from −40.8 % to a range of −34.6 to −27.7 %), with large differences between CB05–CB6 and SAPRC07 due to large differences in their OH and HO2 predictions. An advanced aerosol activation parameterization based on the Fountoukis and Nenes (2005) series reduces the large negative CDNC bias associated with the default Abdul Razzak and Ghan (2000) parameterization from −35.4 % to a range of −0.8 to 7.1 %. However, it increases the errors due to overpredictions of CDNC, mainly over the northeastern US. This work indicates a need to improve other aerosol–cloud–radiation processes in the model, such as the spatial distribution of aerosol optical depth and cloud condensation nuclei, in order to further improve CDNC predictions.

Diurnal evolution of organic aerosol over boreal and tropical forests The first research question of this thesis is: how do local surface forcings and large-scale meteorological forcings shape the evolution of organic aerosol over the boreal and tropical forest? This question is dealt with in Chapters 3 and 4 in case studies for the boreal and tropical forest, respectively. To answer this question a modeling tool (MXLCH-SOA) is developed, which represents land surface conditions and dynamical and chemical processes that influence the evolution of organic aerosol (OA) in a balanced way. The novelty of our approach is that it combines the dynamics of a convective boundary layer (BL) with a reduced gas-phase chemistry mechanism and a module for gas/particle-partitioning of semi-volatile organic species. The principles and governing equations of this modeling tool are described in Chapter 2 and in the subsequent chapters the simplified chemical reaction schemes are presented to calculate secondary organic aerosol (SOA) formation from terpenes (Chapter 3 and 4) and from isoprene (Chapter 4). Despite its simplicity, MXLCH-SOA is able to satisfactorily reproduce the main observed characteristics of dynamics, gas-phase chemistry and gas/particle partitioning for the two studied forest ecosystems and it enables us to explain the temporal variability of the concentrations of organic aerosol and its precursors as a function of the various processes. In short, the results show that the diurnal evolution of organic aerosol in a boreal and a tropical forest is the net result of land surface conditions, boundary layer dynamics, chemical transformations and gas/particle partitioning. In the case study for the boreal forest, the entrainment term of the background OA dominates the OA tendency, while in the tropical forest case it is the interplay of several local and large scale processes that shape the diurnal evolution of OA. A sensitivity analysis for the boreal forest case further shows that the OA concentration is sensitive to both volatile organic compound (VOC) emissions and the partitioning of the surface energy budget into a latent and a sensible heat flux. We have identified two regimes, based on which of the two studied land surface drivers dominates: one in which OA is mainly driven by SOA formation from the emitted VOCs and another in which dilution due to entrainment, as driven by the surface energy fluxes, determines the OA concentration. A background OA to fresh SOA ratio is introduced to facilitate the interpretation of this analysis and is used to quantify the contributions of both fresh and background components to the total OA concentration. One main difference between the two case studies is that in the boreal forest entrainment appears to dominate the diurnal cycle, which leads to a decreasing OA concentration during the day, while in the tropical forest the formation of SOA from both isoprene and terpenes leads to increasing OA concentrations during day time. The MXLCH-SOA framework therefore shows the need to represent all these biochemical and physical processes simultaneously in order to understand the diurnal evolution of OA. As the boundary layer dynamics-chemistry system is not a closed system, it is necessary to further study the influence of external forcings on the diurnal evolution of OA, besides the surface forcings. Two types of large-scale meteorological forcings and their effects on OA evolution through their impact on BL dynamics have been studied: subsidence due to the presence of a high pressure system and advection of relatively cool air. In Chapter 3 a theoretical sensitivity analysis is given of OA evolution to subsidence, which is applied to the tropical forest case study in Chapter 4. Subsidence has a rather counter-intuitive effect on OA concentrations: even though it suppresses the growth of the BL and consequently decreases the mixing volume for chemical species, it leads to decreased OA concentrations. The reason for this is that entrainment is strongly enhanced in case of subsidence due to thermodynamic effects, which results in a stronger dilution of OA. This knowledge is applied in the case study for the tropical forest in Chapter 4, since results from a large-scale model show subsiding air motions over the measurement site and surroundings at Borneo. In addition to subsidence, the advection of cool air is needed to reproduce the observed boundary layer dynamics at Borneo: only if subsidence and advection of relatively cool air are accounted for, the observed low BL height can be reconciled with the large observed surface sensible and latent heat flux. This cool air suppresses BL growth and entrainment. Consequently, the aerosol is trapped in a shallower layer, which leads to an increased concentration compared to the case without advection of cooler air. In conclusion, the large-scale meteorological forcings subsidence and advection of cool air have opposing effects on the diurnal evolution of OA, even though both suppress BL growth. These findings show the utility of our method in identifying effects that should be accounted for in large-scale chemistry transport models. The second research question is whether recently discovered pathways of isoprene chemistry are the key to closing the gap between measured and modeled organic aerosol concentrations in tropical forests and other high isoprene environments. To address this issue, several mechanisms which may affect SOA formation from isoprene are implemented in MXLCH-SOA and discussed in Chapter 4. The hydroxyl radical (OH), the main oxidant of isoprene, is thought to be regenerated in the oxidation of isoprene. We find that for the tropical forest case study, we cannot reconcile the modeled concentrations of VOCs, OH and OA with their observed concentrations and fluxes both for cases with and without OH recycling. Therefore, we conclude that the issue of recycling of the OH radical in the oxidation of isoprene has to be solved before its effect on SOA formation can be determined. The formation of SOA from isoprene involves multiple generations of oxidation and due to this complex chemistry there is no single mechanism which can explain SOA formation from isoprene under all conditions. To gain understanding in this issue, we have implemented different pathways through which isoprene SOA is known to form, although we do not explicitly account for the detailed isoprene oxidation chain. A central aspect of this branching approach is whether the isoprene peroxy radical chemistry follows the low- or the high-NOx pathway. We find that the latter channel dominates in our case study. For SOA formed through the high-NOx channel, we further account for the effect of the NO2/NO ratio on SOA yields. In the presented case study this has little effect as this ratio is low, it but could be more important in regions with slower photochemistry or higher emissions of anthropogenic pollution. In the low-NOx regime, isoprene epoxides (IEPOX) are important intermediate gas-phase species in the formation of isoprene SOA. Even though the low-NOx pathway is only a minor one here, the amount of IEPOX SOA formed is likely substantial, although a better understanding of the exact mechanisms for its formation is needed to confirm this. However, as in previous studies we systematically underestimate the organic aerosol concentration in a tropical forest even though we incorporate the state-of-the-art knowledge on isoprene SOA formation in MXLCH-SOA. Nevertheless, we advocate accounting for NOx regime specific chemical pathways when modeling isoprene SOA formation. As this field is rapidly evolving in terms of the development of new measurement techniques and the discovery of chemical mechanisms, we strongly recommend the intensive use of our modeling system to gain further understanding of the diurnal variability of OA and for testing new hypotheses under atmospheric conditions. Satellite observations of cloud droplet concentration over the boreal forest The final objective of this thesis is to understand how aerosols and meteorological factors influence cloud droplet concentration over the boreal forest. This is a first step in translating the process understanding such as addressed in the previous chapters to larger spatio-temporal scales. Since this objective considers different temporal and spatial scales, a different method is applied in Chapter 5 than in the foregoing chapters. Observations of cloud properties by the MODIS instrument onboard the Terra satellite are combined with a model that contains the microphysics and thermodynamics of a single-layered water cloud to obtain a seasonal cycle of cloud droplet number concentrations, averaged over 9 years of data. This seasonal cycle in cloud droplet concentration is compared with aerosol concentrations at the surface and meteorological fields from ECMWF reanalysis. We find that the cloud droplet number concentration is related to the potential temperature gradient in the boundary layer, a measure for the strength of convection, while it shows no clear relationship with the cloud active aerosol concentration at the surface. From this we conclude that the convective transport of the aerosols from the surface to cloud base is the limiting factor for their activation as cloud droplets. However, convection will also influence the formation of clouds from a thermodynamic perspective. Therefore, it is likely that convection, as driven by land surface conditions, regulates both transport of aerosols to cloud base and the height of the cloud base, defined as the height at which water vapor reaches its saturation pressure. To ultimately understand the effect of the boreal forest on cloud properties, the effects of aerosols and thermodynamics should be studied simultaneously.

Abstract. Organic aerosol (OA) simulations using the volatility basis-set approach were made for East Asia and its outflow region. Model simulations were evaluated through comparisons with OA measured by aerosol mass spectrometers in and around Tokyo (at Komaba and Kisai in summer 2003 and 2004) and over the outflow region in East Asia (at Fukue and Hedo in spring 2009). The simulations with aging processes of organic vapors reproduced the mass concentrations, temporal variations, and formation efficiencies of observed OA at all of the sites reasonably well. As OA mass was severely underestimated in the simulations without the aging processes, the oxidations of organic vapors are essential for reasonable OA simulations over East Asia. By considering the aging processes, simulated OA concentrations increased from 0.24 to 1.28 μg m−3 in the boundary layer over the whole of East Asia. OA formed from the interaction of anthropogenic and biogenic sources was also enhanced by the aging processes. The fraction of controllable OA was estimated to be 87% of total OA over the whole of East Asia, which indicated that most of the OA in our simulations were formed anthropogenically (from controllable combustion sources). A large portion of biogenic secondary OA (78% of biogenic secondary OA) was formed through the influence of anthropogenic sources. These fractions were higher than the fraction of anthropogenic emissions. An important reason for these higher controllable fractions was higher oxidant concentrations and the resulting faster oxidation rates of OA precursors by considering anthropogenic sources. Both the amounts (from 0.18 to 1.12 μg m−3) and the fraction (from 75 to 87%) of controllable OA were increased by aging processes of organic vapors over East Asia.

Simulation of secondary organic aerosol over the Yangtze River Delta region: The impacts from the emissions of intermediate volatility organic compounds and the SOA modeling framework

Abstract. Within minutes after emission, complex photochemistry in biomass burning smoke plumes can cause large changes in the concentrations of ozone (O3) and organic aerosol (OA). Being able to understand and simulate this rapid chemical evolution under a wide variety of conditions is a critical part of forecasting the impact of these fires on air quality, atmospheric composition, and climate. Here we use version 2.1 of the Aerosol Simulation Program (ASP) to simulate the evolution of O3 and secondary organic aerosol (SOA) within a young biomass burning smoke plume from the Williams prescribed fire in chaparral, which was sampled over California in November 2009. We demonstrate the use of a method for simultaneously accounting for the impact of the unidentified intermediate volatility, semi-volatile, and extremely low volatility organic compounds (here collectively called "SVOCs") on the formation of OA (using the Volatility Basis Set – VBS) and O3 (using the concept of mechanistic reactivity). We show that this method can successfully simulate the observations of O3, OA, NOx, ethylene (C2H4), and OH to within measurement uncertainty using reasonable assumptions about the average chemistry of the unidentified SVOCs. These assumptions were (1) a reaction rate constant with OH of ~ 10-11 cm3 s−1; (2) a significant fraction (up to ~ 50 %) of the RO2 + NO reaction resulted in fragmentation, rather than functionalization, of the parent SVOC; (3) ~ 1.1 molecules of O3 were formed for every molecule of SVOC that reacted; (4) ~ 60 % of the OH that reacted with the unidentified non-methane organic compounds (NMOC) was regenerated as HO2; and (5) that ~ 50 % of the NO that reacted with the SVOC peroxy radicals was lost, presumably to organic nitrate formation. Additional evidence for the fragmentation pathway is provided by the observed rate of formation of acetic acid (CH3COOH), which is consistent with our assumed fragmentation rate. However, the model overestimates peroxyacetyl nitrate (PAN) formation downwind by about 50 %, suggesting the need for further refinements to the chemistry. This method could provide a way for classifying different smoke plume observations in terms of the average chemistry of their SVOCs, and could be used to study how the chemistry of these compounds (and the O3 and OA they form) varies between plumes.

Abstract. A detailed three-dimensional regional chemical transport model (Particulate Matter Comprehensive Air Quality Model with Extensions, PMCAMx) was applied over Europe, focusing on the formation and chemical transformation of organic matter. Three periods representative of different seasons were simulated, corresponding to intensive field campaigns. An extensive set of AMS measurements was used to evaluate the model and, using factor-analysis results, gain more insight into the sources and transformations of organic aerosol (OA). Overall, the agreement between predictions and measurements for OA concentration is encouraging, with the model reproducing two-thirds of the data (daily average mass concentrations) within a factor of 2. Oxygenated OA (OOA) is predicted to contribute 93% to total OA during May, 87% during winter and 96% during autumn, with the rest consisting of fresh primary OA (POA). Predicted OOA concentrations compare well with the observed OOA values for all periods, with an average fractional error of 0.53 and a bias equal to −0.07 (mean error = 0.9 μg m−3, mean bias = −0.2 μg m−3). The model systematically underpredicts fresh POA at most sites during late spring and autumn (mean bias up to −0.8 μg m−3). Based on results from a source apportionment algorithm running in parallel with PMCAMx, most of the POA originates from biomass burning (fires and residential wood combustion), and therefore biomass burning OA is most likely underestimated in the emission inventory. The sensitivity of POA predictions to the corresponding emissions' volatility distribution is discussed. The model performs well at all sites when the Positive Matrix Factorization (PMF)-estimated low-volatility OOA is compared against the OA with saturation concentrations of the OA surrogate species C* ≤ 0.1 μg m−3 and semivolatile OOA against the OA with C* > 0.1 μg m−3.

Abstract. Secondary organic aerosol (SOA) is a major component of particulate matter but is often underpredicted in chemistry climate models. Recent advances in measuring and resolving the chemically complex structure of intermediate volatile organic compounds (IVOC) have shown that IVOCs, despite their high SOA yields, have long been underrepresented in models. These compounds are key precursors of SOA from emissions in the road transport sector and significantly influence SOA formation. Understanding vehicle emissions, their chemistry, and their SOA-forming potential is essential for accurately estimating their contributions to atmospheric SOA and global organic aerosol loads. To improve this understanding, we have updated the organic module ORACLE in the global chemistry climate model EMAC. The existing IVOC representation was based on scaled organic carbon (OC) emissions and a highly parameterized volatility basis set (VBS) which underestimated global IVOC emissions, particularly those from gasoline combustion, and oversimplified their chemistry. Here, we replaced this approach with a lumped species framework, in which experimental data for gasoline and diesel emissions were grouped into seven lumped species based on their chemical properties and hydroxylation potentials. These species were linked to adjusted emission inventories for regional diesel and gasoline consumption. A 10 year simulation with the updated ORACLE-IVOC model resulted in significant changes. The global atmospheric burden of road transport IVOC-derived SOA (SOA-iv) increased by 1 order of magnitude, from 0.014 to 0.13 Tg. The composition of road transport organic aerosol (OA) shifted, with SOA-iv contributing 2.5 to 13 times more than the primary organic aerosol (POA) and SOA derived from semi-volatile organic compounds combined. In the results using the previous model, this ratio was between 0.4 and 1.1. The geographical distribution of OA also changed. Regions rich in gasoline relative to diesel emissions experienced higher concentration increases, and remote areas experienced elevated concentrations due to more efficient long-range transport of the new lumped IVOC species. Overall, these changes led to a significant increase in the contribution of road transport to total anthropogenic SOA-iv from an average value of 3 % to 35 %.

Abstract. The spatial distribution and properties of submicron organic aerosol (OA) are among the key sources of uncertainty in our understanding of aerosol effects on climate. Uncertainties are particularly large over remote regions of the free troposphere and Southern Ocean, where very few data have been available and where OA predictions from AeroCom Phase II global models span 2 to 3 orders of magnitude, greatly exceeding the model spread over source regions. The (nearly) pole-to-pole vertical distribution of non-refractory aerosols was measured with an aerosol mass spectrometer onboard the NASA DC-8 aircraft as part of the Atmospheric Tomography (ATom) mission during the Northern Hemisphere summer (August 2016) and winter (February 2017). This study presents the first extensive characterization of OA mass concentrations and their level of oxidation in the remote atmosphere. OA and sulfate are the major contributors by mass to submicron aerosols in the remote troposphere, together with sea salt in the marine boundary layer. Sulfate was dominant in the lower stratosphere. OA concentrations have a strong seasonal and zonal variability, with the highest levels measured in the lower troposphere in the summer and over the regions influenced by biomass burning from Africa (up to 10 µg sm−3). Lower concentrations (∼0.1–0.3 µg sm−3) are observed in the northern middle and high latitudes and very low concentrations (<0.1 µg sm−3) in the southern middle and high latitudes. The ATom dataset is used to evaluate predictions of eight current global chemistry models that implement a variety of commonly used representations of OA sources and chemistry, as well as of the AeroCom-II ensemble. The current model ensemble captures the average vertical and spatial distribution of measured OA concentrations, and the spread of the individual models remains within a factor of 5. These results are significantly improved over the AeroCom-II model ensemble, which shows large overestimations over these regions. However, some of the improved agreement with observations occurs for the wrong reasons, as models have the tendency to greatly overestimate the primary OA fraction and underestimate the secondary fraction. Measured OA in the remote free troposphere is highly oxygenated, with organic aerosol to organic carbon (OA ∕ OC) ratios of ∼2.2–2.8, and is 30 %–60 % more oxygenated than in current models, which can lead to significant errors in OA concentrations. The model–measurement comparisons presented here support the concept of a more dynamic OA system as proposed by Hodzic et al. (2016), with enhanced removal of primary OA and a stronger production of secondary OA in global models needed to provide better agreement with observations.

Abstract. The Station for Measuring Ecosystem–Atmosphere Relations (SMEAR) II, located within the boreal forest of Finland, is a unique station in the world due to the wide range of long-term measurements tracking the Earth–atmosphere interface. In this study, we characterize the composition of organic aerosol (OA) at SMEAR II by quantifying its driving constituents. We utilize a multi-year data set of OA mass spectra measured in situ with an Aerosol Chemical Speciation Monitor (ACSM) at the station. To our knowledge, this mass spectral time series is the longest of its kind published to date. Similarly to other previously reported efforts in OA source apportionment from multi-seasonal or multi-annual data sets, we approached the OA characterization challenge through positive matrix factorization (PMF) using a rolling window approach. However, the existing methods for extracting minor OA components were found to be insufficient for our rather remote site. To overcome this issue, we tested a new statistical analysis framework. This included unsupervised feature extraction and classification stages to explore a large number of unconstrained PMF runs conducted on the measured OA mass spectra. Anchored by these results, we finally constructed a relaxed chemical mass balance (CMB) run that resolved different OA components from our observations. The presented combination of statistical tools provided a data-driven analysis methodology, which in our case achieved robust solutions with minimal subjectivity. Following the extensive statistical analyses, we were able to divide the 2012–2019 SMEAR II OA data (mass concentration interquartile range (IQR): 0.7, 1.3, and 2.6 µg m−3) into three sub-categories – low-volatility oxygenated OA (LV-OOA), semi-volatile oxygenated OA (SV-OOA), and primary OA (POA) – proving that the tested methodology was able to provide results consistent with literature. LV-OOA was the most dominant OA type (organic mass fraction IQR: 49 %, 62 %, and 73 %). The seasonal cycle of LV-OOA was bimodal, with peaks both in summer and in February. We associated the wintertime LV-OOA with anthropogenic sources and assumed biogenic influence in LV-OOA formation in summer. Through a brief trajectory analysis, we estimated summertime natural LV-OOA formation of tens of ng m−3 h−1 over the boreal forest. SV-OOA was the second highest contributor to OA mass (organic mass fraction IQR: 19 %, 31 %, and 43 %). Due to SV-OOA's clear peak in summer, we estimate biogenic processes as the main drivers in its formation. Unlike for LV-OOA, the highest SV-OOA concentrations were detected in stable summertime nocturnal surface layers. Two nearby sawmills also played a significant role in SV-OOA production as also exemplified by previous studies at SMEAR II. POA, taken as a mix of two different OA types reported previously, hydrocarbon-like OA (HOA) and biomass burning OA (BBOA), made up a minimal OA mass fraction (IQR: 2 %, 6 %, and 13 %). Notably, the quantification of POA at SMEAR II using ACSM data was not possible following existing rolling PMF methodologies. Both POA organic mass fraction and mass concentration peaked in winter. Its appearance at SMEAR II was linked to strong southerly winds. Similar wind direction and speed dependence was not observed among other OA types. The high wind speeds probably enabled the POA transport to SMEAR II from faraway sources in a relatively fresh state. In the event of slower wind speeds, POA likely evaporated and/or aged into oxidized organic aerosol before detection. The POA organic mass fraction was significantly lower than reported by aerosol mass spectrometer (AMS) measurements 2 to 4 years prior to the ACSM measurements. While the co-located long-term measurements of black carbon supported the hypothesis of higher POA loadings prior to year 2012, it is also possible that short-term (POA) pollution plumes were averaged out due to the slow time resolution of the ACSM combined with the further 3 h data averaging needed to ensure good signal-to-noise ratios (SNRs). Despite the length of the ACSM data set, we did not focus on quantifying long-term trends of POA (nor other components) due to the high sensitivity of OA composition to meteorological anomalies, the occurrence of which is likely not normally distributed over the 8-year measurement period. Due to the unique and realistic seasonal cycles and meteorology dependences of the independent OA subtypes complemented by the reasonably low degree of unexplained OA variability, we believe that the presented data analysis approach performs well. Therefore, we hope that these results encourage also other researchers possessing several-year-long time series of similar data to tackle the data analysis via similar semi- or unsupervised machine-learning approaches. This way the presented method could be further optimized and its usability explored and evaluated also in other environments.