Secondary Organic Aerosol Model Research Articles

An updated modeling framework to simulate Los Angeles air quality – Part 1: Model development, evaluation, and source apportionment

Abstract. This study describes a modeling framework, model evaluation, and source apportionment to understand the causes of Los Angeles (LA) air pollution. A few major updates are applied to the Community Multiscale Air Quality (CMAQ) model with a high spatial resolution (1 km × 1 km). The updates include dynamic traffic emissions based on real-time, on-road information and recent emission factors and secondary organic aerosol (SOA) schemes to represent volatile chemical products (VCPs). Meteorology is well predicted compared to ground-based observations, and the emission rates from multiple sources (i.e., on-road, volatile chemical products, area, point, biogenic, and sea spray) are quantified. Evaluation of the CMAQ model shows that ozone is well predicted despite inaccuracies in nitrogen oxide (NOx) predictions. Particle matter (PM) is underpredicted compared to concurrent measurements made with an aerosol mass spectrometer (AMS) in Pasadena. Inorganic aerosol is well predicted, while SOA is underpredicted. Modeled SOA consists of mostly organic nitrates and products from oxidation of alkane-like intermediate volatility organic compounds (IVOCs) and has missing components that behave like less-oxidized oxygenated organic aerosol (LO-OOA). Source apportionment demonstrates that the urban areas of the LA Basin and vicinity are NOx-saturated (VOC-sensitive), with the largest sensitivity of O3 to changes in VOCs in the urban core. Differing oxidative capacities in different regions impact the nonlinear chemistry leading to PM and SOA formation, which is quantified in this study.

Global PM2.5 and secondary organic aerosols (SOA) levels with sectorial contribution to anthropogenic and biogenic SOA formation

This study estimates global PM2.5 and anthropogenic and biogenic Secondary Organic Aerosols (a-SOA and b-SOA) and sources contributing to their formation. The global landscape was divided into eleven domains (North America (NAM); South America (SAM); Europe (EUR); North Africa and Middle East (NAF); Equatorial Africa (EAF); South of Africa (SAF); Russia and Central Asia (RUS); Eastern Asia (EAS); South Asia (SAS); Southeast Asia (SEA) and Australia (AUS)) and 46 cities based on varying populations. Three inventories for global emissions were considered: Community Emissions Data System, Model of Emission of Gases and Aerosol, and Global Fire Emissions Database. WRF-Chem model coupled with atmospheric reactions and the secondary organic aerosol model was employed for estimating PM2.5, a-SOA, and b-SOA for 2018. No city attained WHO's annual PM2.5 guideline of 5 μg/m3. Delhi, Dhaka, and Kolkata (63–92 μg/m3) in south Asia were the most polluted, and seven cities (mostly in EUR and NAM) met the WHO target IV (10 μg/m3). The highest SOA levels (2–9 μg/m3) were in the cities of SAS and Africa, but with a low SOA contribution to PM2.5 (3–22%). However, the low levels of SOA (1–3 μg/m3) in EUR and NAM had a higher contribution of SOA to PM2.5 (20–33%). b-SOA were consistent with the region's vegetation and forest cover. The SOA contribution was dominated by residential emissions in all domains (except in the NAF and AUS) (maximum in SAS). The non-coal industry was the second highest contributor (except in EAF, NAF, and AUS) and EUR had the maximum contribution from agriculture and transport. Globally, residential and industry (non-coal and coal) sectors showed the maximum contribution to SOA, with a-SOA and b-SOA being nearly equal. Ridding of biomass and residential burning of solid fuel is the single most action benefiting the PM2.5 and SOA concerns.

Advecting Superspecies: Efficiently Modeling Transport of Organic Aerosol With a Mass‐Conserving Dimensionality Reduction Method

AbstractThe chemical transport model LOTOS‐EUROS uses a volatility basis set (VBS) approach to represent the formation of secondary organic aerosol (SOA) in the atmosphere. Inclusion of the VBS approximately doubles the dimensionality of LOTOS‐EUROS and slows computation of the advection operator by a factor of two. This complexity limits SOA representation in operational forecasts. We develop a mass‐conserving dimensionality reduction method based on matrix factorization to find latent patterns in the VBS tracers that correspond to a smaller set of superspecies. Tracers are reversibly compressed to superspecies before transport, and the superspecies are subsequently decompressed to tracers for process‐based SOA modeling. This physically interpretable data‐driven method conserves the total concentration and phase of the tracers throughout the process. The superspecies approach is implemented in LOTOS‐EUROS and found to accelerate the advection operator by a factor of 1.5–1.8. Concentrations remain numerically stable over model simulation times of 2 weeks, including simulations at higher spatial resolutions than the data‐driven models were trained on. The reversible compression of VBS tracers enables detailed, process‐based SOA representation in LOTOS‐EUROS operational forecasts in a computationally efficient manner. Beyond this case study, the physically consistent data‐driven approach developed in this work enforces conservation laws that are essential to other Earth system modeling applications, and generalizes to other processes where computational benefit can be gained from a two‐way mapping between detailed process variables and their representation in a reduced‐dimensional space.

Modeling of wintertime regional formation of secondary organic aerosols around Beijing: sensitivity analysis and anthropogenic contributions

Modeling of secondary organic aerosol (SOA) has remained a big challenge due to the various precursors and complex processes involved. In this study, the WRF-CAMx model was used to predict the ambient SOA concentrations in urban Beijing as well as the North China Plain (NCP) during a polluted period in winter. To identify the major uncertainties and improve the model performance, a series of model tests were performed to assess the sensitivity of model prediction to the key factors. Then the sources of SOA in Beijing were identified using the optimized model. Both the volatility basis set (VBS) approach and the two-product approach were used for SOA simulation. Although the modeled SOA was underpredicted compared with the SOA estimated through filter-based measurements, the VBS scheme produced higher SOA than the traditional two-product scheme. According to the sensitivity tests with the VBS scheme, the emissions of volatile organic compounds (VOC) and intermediate volatility organic compounds (IVOC) as well as the oxidant levels were the key factors that affected SOA prediction. Based on the optimized simulation scenario, the potential contributions from different anthropogenic sources and source areas were identified, with over 80% of SOA in urban Beijing from regional transport of SOA or its precursors from the surrounding areas during the polluted period. Residential emission in the North China Plain appeared as the dominant source of SOA in urban Beijing from the perspective of regional contribution.Graphical

Explosive formation of secondary organic aerosol due to aerosol-fog interactions

Aerosol particles can profoundly affect the local environment and global climate. Explosive growths of secondary organic aerosol (SOA) are frequently observed during serious haze evens, but their fundamental mechanism remains unclear. We used chamber experiments and kinetic model simulations to reveal the microphysical mechanism for explosive organic aerosol formation. The evolution of SOA with organic vapors under dry and highly humid conditions was determined based on a high-resolution Orbitrap mass spectrometer. We found that the condensation of gas-phase organics could lead to the formation of cloud or fog droplets with relative humidity below 100 %; meanwhile, the aerosol-fog interaction could result in the explosive growth of SOA. Monomeric products from toluene oxidation were verified to primarily contribute to the increased SOA in super humid conditions, which are mainly assigned to be intermediate- and semi-volatile organic compounds. Moreover, we demonstrated that the decreasing temperatures could dramatically amplify organic compounds' co-condensing influence on SOA explosive formation and activation at relative humidity above 85 % and temperature below 20 °C. Our findings revealed that aerosol-fog interaction is the fundamental driving force for explosive organic aerosol formation. It indicates that overlooking the co-condensation of organic vapors with water could significantly underestimate SOA and liquid water content in 3D models.

Secondary organic aerosol formation via multiphase reaction of hydrocarbons in urban atmospheres using CAMx integrated with the UNIPAR model

Abstract. The prediction of secondary organic aerosol (SOA) on regional scales is traditionally performed by using gas–particle partitioning models. In the presence of inorganic salted wet aerosols, aqueous reactions of semivolatile organic compounds can also significantly contribute to SOA formation. The UNIfied Partitioning-Aerosol phase Reaction (UNIPAR) model utilizes the explicit gas mechanism to better predict SOA formation from multiphase reactions of hydrocarbons. In this work, the UNIPAR model was incorporated with the Comprehensive Air Quality Model with Extensions (CAMx) to predict the ambient concentration of organic matter (OM) in urban atmospheres during the Korean-United States Air Quality (2016 KORUS-AQ) campaign. The SOA mass predicted with CAMx–UNIPAR changed with varying levels of humidity and emissions and in turn has the potential to improve the accuracy of OM simulations. CAMx–UNIPAR significantly improved the simulation of SOA formation under the wet condition, which often occurred during the KORUS-AQ campaign, through the consideration of aqueous reactions of reactive organic species and gas–aqueous partitioning. The contribution of aromatic SOA to total OM was significant during the low-level transport/haze period (24–31 May 2016) because aromatic oxygenated products are hydrophilic and reactive in aqueous aerosols. The OM mass predicted with CAMx–UNIPAR was compared with that predicted with CAMx integrated with the conventional two-product model (SOAP). Based on estimated statistical parameters to predict OM mass, the performance of CAMx–UNIPAR was noticeably better than that of the conventional CAMx model, although both SOA models underestimated OM compared to observed values, possibly due to missing precursor hydrocarbons such as sesquiterpenes, alkanes, and intermediate volatile organic compounds (VOCs). The CAMx–UNIPAR simulation suggested that in the urban areas of South Korea, terpene and anthropogenic emissions significantly contribute to SOA formation while isoprene SOA minimally impacts SOA formation.

NMVOC emissions and their formation into secondary organic aerosols over India using WRF-Chem model

Non-methane volatile organic compounds (NMVOC) emissions from anthropogenic, open burning, and biogenic sources, and their formation into secondary organic aerosols (SOA) over India have been examined. The emissions databases EDGAR-HTAP (v2.2), Fire Inventory from NCAR (FINN), and the Model of Emission of Gases and Aerosol (MEGAN) were used. India emits 3% of annual global NMVOC at 30.45 Tg (anthropogenic:8.97; open burning: 2.01; biogenic: 19.47 Tg/year). Spatial and temporal variations for SOA were simulated using the WRF-Chem model combined with a secondary organic aerosol model for the year 2018. WRF-Chem model was expanded to include a scheme of parameterization for estimating isoprene-derived SOA. Annual estimated SOA production was 2.93 Tg/year, 4% of the global SOA.The annual SOA concentration over India was 3.19 ± 2.18 μg/m3 (post-monsoon: 5.16 ± 3.29, winter: 3.74 ± 2.55, summer: 2.62 ± 1.78, and monsoon: 1.24 ± 0.84 μg/m3). WRF-Chem model underestimated the SOA levels, possibly due to incomplete emission inventories and missing NMVOC precursors, including biogenic and primary organic carbon emissions that add to SOA formation. The annual anthropogenic and biogenic SOA had a near equal contribution of 50% mostly from populated regions and evergreen forests.

Isoprene-Chlorine Oxidation in the Presence of NOx and Implications for Urban Atmospheric Chemistry.

Fine particulate matter (PM2.5) is a key indicator of urban air quality. Secondary organic aerosol (SOA) contributes substantially to the PM2.5 concentration. Discrepancies between modeling and field measurements of SOA indicate missing sources and formation mechanisms. Recent studies report elevated concentrations of reactive chlorine species in inland and urban regions, which increase the oxidative capacity of the atmosphere and serve as sources for SOA and particulate chlorides. Chlorine-initiated oxidation of isoprene, the most abundant nonmethane hydrocarbon, is known to produce SOA under pristine conditions, but the effects of anthropogenic influences in the form of nitrogen oxides (NOx) remain unexplored. Here, we investigate chlorine-isoprene reactions under low- and high-NOx conditions inside an environmental chamber. Organic chlorides including C5H11ClO3, C5H9ClO3, and C5H9ClO4 are observed as major gas- and particle-phase products. Modeling and experimental results show that the secondary OH-isoprene chemistry is significantly enhanced under high-NOx conditions, accounting for up to 40% of all isoprene oxidized and leading to the suppression of organic chloride formation. Chlorine-initiated oxidation of isoprene could serve as a source for multifunctional (chlorinated) organic oxidation products and SOA in both pristine and anthropogenically influenced environments.

Phase Behavior of Internal Mixtures of Hydrocarbon-like Primary Organic Aerosol and Secondary Aerosol Based on Their Differences in Oxygen-to-Carbon Ratios.

The phase behavior, the number and type of phases, in atmospheric particles containing mixtures of hydrocarbon-like organic aerosol (HOA) and secondary organic aerosol (SOA) is important for predicting their impacts on air pollution, human health, and climate. Using a solvatochromic dye and fluorescence microscopy, we determined the phase behavior of 11 HOA proxies (O/C = 0–0.29) each mixed with 7 different SOA materials generated in environmental chambers (O/C 0.4–1.08), where O/C represents the average oxygen-to-carbon atomic ratio. Out of the 77 different HOA + SOA mixtures studied, we observed two phases in 88% of the cases. The phase behavior was independent of relative humidity over the range between 90% and <5%. A clear trend was observed between the number of phases and the difference between the average O/C ratios of the HOA and SOA components (ΔO/C). Using a threshold ΔO/C of 0.265, we were able to predict the phase behavior of 92% of the HOA + SOA mixtures studied here, with one-phase particles predicted for ΔO/C < 0.265 and two-phase particles predicted for ΔO/C ≥ 0.265. The threshold ΔO/C value provides a relatively simple and computationally inexpensive framework for predicting the number of phases in internal SOA and HOA mixtures in atmospheric models.

Modeling secondary organic aerosol formation from volatile chemical products.

Volatile chemical products (VCPs) are commonly-used consumer and industrial items that are an important source of anthropogenic emissions. Organic compounds from VCPs evaporate on atmospherically relevant time scales and include many species that are secondary organic aerosol (SOA) precursors. However, the chemistry leading to SOA, particularly that of intermediate volatility organic compounds (IVOCs), has not been fully represented in regional-scale models such as the Community Multiscale Air Quality (CMAQ) model, which tend to underpredict SOA concentrations in urban areas. Here we develop a model to represent SOA formation from VCP emissions. The model incorporates a new VCP emissions inventory and employs three new classes of emissions: siloxanes, oxygenated IVOCs, and nonoxygenated IVOCs. VCPs are estimated to produce 1.67 μg m-3 of noontime SOA, doubling the current model predictions and reducing the SOA mass concentration bias from -75% to -58% when compared to observations in Los Angeles in 2010. While oxygenated and nonoxygenated intermediate volatility VCP species are emitted in similar quantities, SOA formation is dominated by the nonoxygenated IVOCs. Formaldehyde and SOA show similar relationships to temperature and bias signatures indicating common sources and/or chemistry. This work suggests that VCPs contribute up to half of anthropogenic SOA in Los Angeles and models must better represent SOA precursors from VCPs to predict the urban enhancement of SOA.

Prediction of Phase State of Secondary Organic Aerosol Internally Mixed with Aqueous Inorganic Salts.

In the presence of inorganic salts, secondary organic aerosol (SOA) undergoes liquid-liquid phase separation (LLPS), liquid-solid phase separation, or a homogeneous phase in ambient air. In this study, a regression model was derived to predict aerosol phase separation relative humidity (SRH) for various organic and inorganic mixes. The model implemented organic physicochemical parameters (i.e., oxygen to carbon ratio, molecular weight, and hydrogen-bonding ability) and the parameters related to inorganic compositions (i.e., ammonium, sulfate, nitrate, and water). The aerosol phase data were observed using an optical microscope and also collected from the literature. The crystallization of aerosols at the effloresce RH (ERH) was semiempirically predicted with a neural network model. Overall, the greater SRH appeared for the organic compounds with the lower oxygen to carbon ratios or the greater molecular weight and the higher aerosol acidity or the larger fraction of inorganic nitrate led to the lower SRH. The resulting model has been demonstrated for three different chamber-generated SOA (originated from β-pinene, toluene, and 1,3,5-trimethylbenzene), which were internally mixed with the inorganic aqueous system of ammonium-sulfate-water. For all three SOA systems, both observations and model predictions showed LLPS at RH <80%. In the urban atmosphere, LLPS is likely a frequent occurrence for the typical anthropogenic SOA, which originates from aromatic and alkane hydrocarbon.

Secondary aerosol formation from a Chinese gasoline vehicle: Impacts of fuel (E10, gasoline) and driving conditions (idling, cruising)

Chassis dynamometer experiments were conducted to investigate the effect of vehicle speed and usage of ethanol-blended gasoline (E10) on formation and evolution of gasoline vehicular secondary organic aerosol (SOA) using a Gothenburg Potential Aerosol Mass (Go: PAM) reactor. The SOA forms rapidly, and its concentration exceeds that of primary organic aerosol (POA) at an equivalent photochemical age (EPA) of ~1 day. The particle effective densities grow from 0.62 ± 0.02 g cm−3 to 1.43 ± 0.07 g cm−3 with increased hydroxyl radical (OH) exposure. The maximum SOA production under idling conditions (4259–7394 mg kg-fuel−1) is ~20 times greater than under cruising conditions. There was no statistical difference between SOA formation from pure gasoline and its formation from E10. The slopes in Van Krevelen diagram indicate that the formation pathways of bulk SOA includes the addition of both alcohol/peroxide functional groups and carboxylic acid formation from fragmentation. A closure estimation of SOA based on bottom-up and top-down methods shows that only 16%–38% of the measured SOA can be explained by the oxidation of measured volatile organic compounds (VOCs), suggesting the existence of missing precursors, e.g. unmeasured VOCs and probably semivolatile or intermediate volatile organic compounds (S/IVOCs). Our results suggest that applying parameters obtained from unified driving cycles to model SOA concentrations may lead to large discrepancies between modeled and ambient vehicular SOA. No reduction in vehicular `SOA production is realized by replacing normal gasoline with E10.

Analysis of secondary organic aerosol simulation bias in the Community Earth System Model (CESM2.1)

Abstract. Organic aerosol (OA) has been considered as one of the most important uncertainties in climate modeling due to the complexity in presenting its chemical production and depletion mechanisms. To better understand the capability of climate models and probe into the associated uncertainties in simulating OA, we evaluate the Community Earth System Model version 2.1 (CESM2.1) configured with the Community Atmosphere Model version 6 (CAM6) with comprehensive tropospheric and stratospheric chemistry representation (CAM6-Chem) through a long-term simulation (1988–2019) with observations collected from multiple datasets in the United States. We find that CESM generally reproduces the interannual variation and seasonal cycle of OA mass concentration at surface layer with a correlation of 0.40 compared to ground observations and systematically overestimates (69 %) in summer and underestimates (−19 %) in winter. Through a series of sensitivity simulations, we reveal that modeling bias is primarily related to the dominant fraction of monoterpene-formed secondary organic aerosol (SOA), and a strong positive correlation of 0.67 is found between monoterpene emission and modeling bias in the eastern US during summer. In terms of vertical profile, the model prominently underestimates OA and monoterpene concentrations by 37 %–99 % and 82 %–99 %, respectively, in the upper air (&gt; 500 m) as validated against aircraft observations. Our study suggests that the current volatility basis set (VBS) scheme applied in CESM might be parameterized with monoterpene SOA yields that are too high, which subsequently results in strong SOA production near the emission source area. We also find that the model has difficulty in reproducing the decreasing trend of surface OA in the southeastern US probably because of employing pure gas VBS to represent isoprene SOA which is in reality mainly formed through multiphase chemistry; thus, the influence of aerosol acidity and sulfate particle change on isoprene SOA formation has not been fully considered in the model. This study reveals the urgent need to improve the SOA modeling in climate models.

Future changes in isoprene-epoxydiol-derived secondary organic aerosol (IEPOX SOA) under the Shared Socioeconomic Pathways: the importance of physicochemical dependency

Abstract. Secondary organic aerosol (SOA) is a dominant contributor of fine particulate matter in the atmosphere, but the complexity of SOA formation chemistry hinders the accurate representation of SOA in models. Volatility-based SOA parameterizations have been adopted in many recent chemistry modeling studies and have shown a reasonable performance compared to observations. However, assumptions made in these empirical parameterizations can lead to substantial errors when applied to future climatic conditions as they do not include the mechanistic understanding of processes but are rather fitted to laboratory studies of SOA formation. This is particularly the case for SOA derived from isoprene epoxydiols (IEPOX SOA), for which we have a higher level of understanding of the fundamental processes than is currently parameterized in most models. We predict future SOA concentrations using an explicit mechanism and compare the predictions with the empirical parameterization based on the volatility basis set (VBS) approach. We then use the Community Earth System Model 2 (CESM2.1.0) with detailed isoprene chemistry and reactive uptake processes for the middle and end of the 21st century under four Shared Socioeconomic Pathways (SSPs): SSP1–2.6, SSP2–4.5, SSP3–7.0, and SSP5–8.5. With the explicit chemical mechanism, we find that IEPOX SOA is predicted to increase on average under all future SSP scenarios but with some variability in the results depending on regions and the scenario chosen. Isoprene emissions are the main driver of IEPOX SOA changes in the future climate, but the IEPOX SOA yield from isoprene emissions also changes by up to 50 % depending on the SSP scenario, in particular due to different sulfur emissions. We conduct sensitivity simulations with and without CO2 inhibition of isoprene emissions that is highly uncertain, which results in factor of 2 differences in the predicted IEPOX SOA global burden, especially for the high-CO2 scenarios (SSP3–7.0 and SSP5–8.5). Aerosol pH also plays a critical role in the IEPOX SOA formation rate, requiring accurate calculation of aerosol pH in chemistry models. On the other hand, isoprene SOA calculated with the VBS scheme predicts a nearly constant SOA yield from isoprene emissions across all SSP scenarios; as a result, it mostly follows isoprene emissions regardless of region and scenario. This is because the VBS scheme does not consider heterogeneous chemistry; in other words, there is no dependency on aerosol properties. The discrepancy between the explicit mechanism and VBS parameterization in this study is likely to occur for other SOA components as well, which may also have dependencies that cannot be captured by VBS parameterizations. This study highlights the need for more explicit chemistry or for parameterizations that capture the dependence on key physicochemical drivers when predicting SOA concentrations for climate studies.

Particle Size Distribution Dynamics Can Help Constrain the Phase State of Secondary Organic Aerosol.

Particle phase state is a property of atmospheric aerosols that has important implications for the formation, evolution, and gas/particle partitioning of secondary organic aerosol (SOA). In this work, we use a size-resolved chemistry and microphysics model (Statistical Oxidation Model coupled to the TwO Moment Aerosol Sectional (SOM-TOMAS)), updated to include an explicit treatment of particle phase state, to constrain the bulk diffusion coefficient (Db) of SOA produced from α-pinene ozonolysis. By leveraging data from laboratory experiments performed in the absence of a seed and under dry conditions, we find that the Db for SOA can be constrained ((1-7) × 10-15 cm2 s-1 in these experiments) by simultaneously reproducing the time-varying SOA mass concentrations and the evolution of the particle size distribution. Another version of our model that used the predicted SOA composition to calculate the glass-transition temperature, viscosity, and, ultimately, Db (∼10-15 cm2 s-1) of the SOA was able to reproduce the mass and size distribution measurements when we included oligomer formation (oligomers accounted for about a fifth of the SOA mass). Our work highlights the potential of a size-resolved SOA model to constrain the particle phase state of SOA using historical measurements of the evolution of the particle size distribution.

Aerosol optical depth assimilation for a modal aerosol model: Implementation and application in AOD forecasts over East Asia

A new aerosol optical depth (AOD) data assimilation (DA) module was developed in Gridpoint Statistical Interpolation (GSI) 3-dimensional variational (3DVAR) system, named FastJ/CRTM-AOD DA module. And applied to the Modal Aerosol Dynamics Model for Europe with the Secondary Organic Aerosol Model (MADE/SORGAM) in the Weather Research and Forecasting/Chemistry model (WRF/Chem). The Fast-J optical module in WRF/Chem was used as the observation operator of AOD. The corresponding Jacobian code was modified from the one of CRTM-AOD in GSI. This way obviated the need for the Jacobian code's generation, which was complex and difficult for the highly nonlinear observation operator. During the studying period (January and April of 2014), compared to the ground AOD observations, AOD DA reduced about 20% fractional error (FE) with MADE/SORGAM. The original DA framework, which applied to the Goddard Chemistry Aerosol Radiation and Transport (GOCART) mechanism, performed slightly better than the new assimilation scheme for the low-value AOD situations (value < 0.4). However, compared to the original DA framework, the new DA scheme show a notable improvement for the high-value (0.4 < value ≤ 1.2) and extreme-high-value (value > 1.2) AOD situations. FE can be reduced by 48% and 64%, respectively. It indicates that the AOD DA impacts on AOD forecasts vary significant between different aerosol mechanisms. Moreover, FastJ/CRTM-AOD DA module can be easily and efficiently applied to the other aerosol schemes and the other optical modules, which is important to the development on AOD assimilation.

Current State of Atmospheric Aerosol Thermodynamics and Mass Transfer Modeling: A Review

A useful aerosol model must be able to adequately resolve the chemical complexity and phase state of the wide particle size range arising from the many different secondary aerosol growth processes to assess their environmental and health impacts. Over the past two decades, significant advances in understanding of gas-aerosol partitioning have occurred, particularly with respect to the role of organic compounds, yet aerosol representations have changed little in air quality and climate models since the late 1990s and early 2000s. The gas-aerosol partitioning models which are still commonly used in air quality models are separate inorganics-only thermodynamics and secondary organic aerosol (SOA) formation based on absorptive partitioning theory with an assumption of well-mixed liquid-like particles that continuously maintain equilibrium with the gas phase. These widely used approaches in air quality models for secondary aerosol composition and growth based on separated inorganic and organic processes are inadequate. This review summarizes some of the important developments during the past two decades in understanding of gas aerosol mass transfer processes. Substantial increases in computer performance in the last decade justify increasing the process detail in aerosol models. Organics play a central role during post-nucleation growth into the accumulation mode and change the hygroscopic properties of sulfate aerosol. At present, combined inorganic-organic aerosol thermodynamics models are too computationally expensive to be used online in 3-D simulations without high levels of aggregation of organics into a small number of functional surrogates. However, there has been progress in simplified modeling of liquid-liquid phase separation (LLPS) and distinct chemical regimes within organic-rich and inorganic-rich phases. Additional limitations of commonly used thermodynamics models are related to lack of surface tension data for various aerosol compositions in the small size limit, and lack of a comprehensive representation of surface interaction terms such as disjoining pressure in the Gibbs free energy which become significant in the small size limit and which affect both chemical composition and particle growth. As a result, there are significant errors in modeling of hygroscopic growth and phase transitions for particles in the nucleation and Aitken modes. There is also increasing evidence of reduced bulk diffusivity in viscous organic particles and, therefore, traditional secondary organic aerosol models, which are typically based on the assumption of instantaneous equilibrium gas-particle partitioning and neglect the kinetic effects, are no longer tenable.

Particle-Phase Diffusion Modulates Partitioning of Semivolatile Organic Compounds to Aged Secondary Organic Aerosol.

The diffusivity of semivolatile organic compounds (SVOCs) in the bulk particle phase of a viscous atmospheric secondary organic aerosol (SOA) can have a profound impact on aerosol growth and size distribution dynamics. Here, we investigate the bulk diffusivity of SVOCs formed from photo-oxidation of isoprene as they partition to a bimodal aerosol consisting of an Aitken (potassium sulfate) and accumulation mode (aged α-pinene SOA) particles as a function of relative humidity (RH). The model analysis of the observed size distribution evolution shows that liquid-like diffusion coefficient values of Db > 10-10 cm2 s-1 fail to explain the growth of the Aitken mode. Instead, much lower values of Db between2.5 × 10-15 cm2 s-1 at 32% RH and 8 × 10-15 cm2 s-1 at 82% RH were needed to successfully reproduce the growth of both modes. The diffusivity within the aged α-pinene SOA remains appreciably slow even at 80% RH, resulting in hindered partitioning of SVOCs to large viscous particles and allowing smaller and relatively less viscous particles to effectively absorb the available SVOCs and grow much faster than would be possible otherwise. These results have important implications for modeling SOA formation and growth in the ambient atmosphere.

Urban Oxidation Flow Reactor Measurements Reveal Significant Secondary Organic Aerosol Contributions from Volatile Emissions of Emerging Importance.

Mobile sampling studies have revealed enhanced levels of secondary organic aerosol (SOA) in source-rich urban environments. While these enhancements can be from rapidly reacting vehicular emissions, it was recently hypothesized that nontraditional emissions (volatile chemical products and upstream emissions) are emerging as important sources of urban SOA. We tested this hypothesis by using gas and aerosol mass spectrometry coupled with an oxidation flow reactor (OFR) to characterize pollution levels and SOA potentials in environments influenced by traditional emissions (vehicular, biogenic), and nontraditional emissions (e.g., paint fumes). We used two SOA models to assess contributions of vehicular and biogenic emissions to our observed SOA. The largest gap between observed and modeled SOA potential occurs in the morning-time urban street canyon environment, for which our model can only explain half of our observation. Contributions from VCP emissions (e.g., personal care products) are highest in this environment, suggesting that VCPs are an important missing source of precursors that would close the gap between modeled and observed SOA potential. Targeted OFR oxidation of nontraditional emissions shows that these emissions have SOA potentials that are similar, if not larger, compared to vehicular emissions. Laboratory experiments reveal large differences in SOA potentials of VCPs, implying the need for further characterization of these nontraditional emissions.

Secondary organic aerosol formation from &amp;lt;i&amp;gt;α&amp;lt;/i&amp;gt;-pinene, alkanes, and oil-sands-related precursors in a new oxidation flow reactor

Abstract. Oil-sands (OS) operations in Alberta, Canada, are a large source of secondary organic aerosol (SOA). However, the SOA formation process from OS-related precursors remains poorly understood. In this work, a newly developed oxidation flow reactor (OFR), the Environment and Climate Change Canada OFR (ECCC-OFR), was characterized and used to study the yields and composition of SOA formed from OH oxidation of α-pinene, selected alkanes, and the vapors evolved from five OS-related samples (OS ore, naphtha, tailings pond water, bitumen, and dilbit). The derived SOA yields from α-pinene and selected alkanes using the ECCC-OFR were in good agreement with those of traditional smog chamber experiments but significantly higher than those of other OFR studies under similar conditions. The results also suggest that gas-phase reactions leading to fragmentation (i.e., C–C bond cleavage) have a relatively small impact on the SOA yields in the ECCC-OFR at high photochemical ages, in contrast to other previously reported OFR results. Translating the impact of fragmentation reactions in the ECCC-OFR to ambient atmospheric conditions reduces its impact on SOA formation even further. These results highlight the importance of careful evaluation of OFR data, particularly when using such data to provide empirical factors for the fragmentation process in models. Application of the ECCC-OFR to OS-related precursor mixtures demonstrated that the SOA yields from OS ore and bitumen vapors (maximum of ∼0.6–0.7) are significantly higher than those from the vapors from solvent use (naphtha), effluent from OS processing (tailings pond water), and from the solvent diluted bitumen (dilbit; maximum of ∼0.2–0.3), likely due to the volatility of each precursor mixture. A comparison of the yields and elemental ratios (H∕C and O∕C) of the SOA from the OS-related precursors to those of linear and cyclic alkane precursors of similar carbon numbers suggests that cyclic alkanes play an important role in the SOA formation in the OS. The analysis further indicates that the majority of the SOA formed downwind of OS facilities is derived from open-pit mining operations (i.e., OS ore evaporative emissions) rather than from higher-volatility precursors from solvent use during processing and/or tailings management. The current results have implications for improving the regional modeling of SOA from OS sources, for the potential mitigation of OS precursor emissions responsible for observed SOA downwind of OS operations, and for the understanding of petrochemical- and alkane-derived SOA in general.