Volatility Bins Research Articles

Volatility Basis Set Distributions and Viscosity of Organic Aerosol Mixtures: Insights from Chemical Characterization Using Temperature-Programmed Desorption-Direct Analysis in Real-Time High-Resolution Mass Spectrometry.

Quantitative assessment of gas-particle partitioning of individual components within complex atmospheric organic aerosol (OA) mixtures is critical for predicting and comprehending the formation and evolution of OA particles in the atmosphere. This investigation leverages previously documented data obtained through a temperature-programmed desorption-direct analysis in real-time, high-resolution mass spectrometry (TPD-DART-HRMS) platform. This methodology facilitates the bottom-up construction of volatility basis set (VBS) distributions for constituents found in three biogenic secondary organic aerosol (SOA) mixtures produced through the ozonolysis of α-pinene, limonene, and ocimene. The apparent enthalpies (ΔH*, kJ mol-1) and saturation mass concentrations (CT*, μg·m-3) of individual SOA components, determined as a function of temperature (T, K), facilitated an assessment of changes in VBS distributions and gas-particle partitioning with respect to T and atmospheric total organic mass loadings (tOM, μg·m-3). The VBS distributions reveal distinct differences in volatilities among monomers, dimers, and trimers, categorized into separate volatility bins. At the ambient temperature of T = 298 K, only monomers efficiently partition between gas and particle phases across a broad range of atmospherically relevant tOM values of 1-100 μg·m-3. Partitioning of dimers and trimers becomes notable only at T > 360 K and T > 420 K, respectively. The viscosity of SOA mixtures is assessed using a bottom-up calculation approach, incorporating the input of elemental formulas, ΔH*, CT*, and particle-phase mass fractions of the SOA components. Through this approach, we are able to accurately estimate the variations in SOA viscosity that result from the evaporation of its components. These variations are, in turn, influenced by atmospherically relevant changes in tOM and T. Comparison of the calculated SOA viscosity and diffusivity values with literature reported experimental results shows close agreement, thereby validating the employed calculation approach. These findings underscore the significant potential for TPD-DART-HRMS measurements in enabling the untargeted analysis of organic molecules within OA mixtures. This approach facilitates quantitative assessment of their gas-particle partitioning and allows for the estimation of their viscosity and condensed-phase diffusion, thereby contributing valuable insights to atmospheric models.

Detailed Speciation of Semi-Volatile and Intermediate-Volatility Organic Compounds (S/IVOCs) in Marine Fuel Oils Using GC × GC-MS.

Ship emissions contribute substantial air pollutants when at berth. However, the complexity and diversity of the marine fuels utilized hinder our understanding and mapping of the characteristics of ship emissions. Herein, we applied GC × GC-MS to analyze the components of marine fuel oils. Owing to the high separation capacity of GC × GC-MS, 11 classes of organic compounds, including b-alkanes, alkenes, and cyclo-alkanes, which can hardly be resolved by traditional one-dimensional GC-MS, were detected. Significant differences are observed between light (-10# and 0#) and heavy (120# and 180#) fuels. Notably, -10# and 0# diesel fuels are more abundant in b-alkanes (44~49%), while in 120# and 180#, heavy fuels b-alkanes only account for 8%. Significant enhancement of naphthalene proportions is observed in heavy fuels (20%) compared to diesel fuels (2~3%). Hopanes are detected in all marine fuels and are especially abundant in heavy marine fuels. The volatility bins, one-dimensional volatility-based set (VBS), and two-dimensional VBS (volatility-polarity distributions) of marine fuel oils are investigated. Although IVOCs still take dominance (62-66%), the proportion of SVOCs in heavy marine fuels is largely enhanced, accounting for ~30% compared to 6~12% in diesel fuels. Furthermore, the SVOC/IVOC ratio could be applied to distinguish light and heavy marine fuel oils. The SVOC/IVOC ratios for -10# diesel fuel, 0# diesel fuel, 120# heavy marine fuel, and 180# heavy marine fuel are 0.085 ± 0.046, 0.168 ± 0.159, 0.504, and 0.439 ± 0.021, respectively. Our work provides detailed information on marine fuel compositions and could be further implemented in estimating organic emissions and secondary organic aerosol (SOA) formation from marine fuel storage and evaporation processes.

Measurement-based intermediate volatility organic compound emission inventory from on-road vehicle exhaust in China

Intermediate volatility organic compounds (IVOCs) have great potential to form secondary organic aerosols (SOA) in the atmosphere. Thus, a high-resolution IVOC emission inventory is essential for the accurate simulation of SOA formation. This study developed the first nationwide on-road vehicular IVOC emission inventory in China based on localized measurement of the IVOC emission factors and volatility distributions for various vehicle types. The total vehicular IVOC emissions in China in 2019 were estimated to be 241.2 Gg. Heavy-duty trucks, light-duty trucks, and light-duty passenger vehicles contributed the most, accounting for 47.6%, 24.6%, and 16.9% of total vehicular IVOC emissions, respectively. Although much higher in number, gasoline vehicles contributed 15.0%, which was far less than the contribution of diesel vehicles. The two peaks in volatility bins B12–B13 and B16–B17 accounted for 42.2% and 23.7% of the total IVOC emissions, respectively. By gridding the emission inventory into a relatively high resolution of 0.1° × 0.1°, high-emission areas and hotspots were clearly identified. In general, eastern China had substantially higher vehicular IVOC emissions than western China. High-emission areas with emission intensity >10 Mg·grid−1 covered most of the North China Plain, Yangtze River Delta, and Pearl River Delta. The emission intensity over the downtown areas of Beijing and Shanghai exceeded 50 Mg·grid−1. In contrast, IVOC emissions over western China were relatively lower, with a network structure gathering around the traffic arteries serving inter-provincial transportation. This study underscored the importance of having a localized emission factor to better reflect the IVOC emission characteristics from Chinese vehicles and to improve the assessment of their environmental impacts.

Identification of two main origins of intermediate-volatility organic compound emissions from vehicles in China through two-phase simultaneous characterization

Intermediate-volatility organic compounds (IVOCs) emitted from vehicles are generally in the gas phase but may partly partition into particle phase when measured under ambient temperature. To have a complete and accurate picture of IVOC emissions from vehicles, gas- and particle-phase IVOCs from a fleet of gasoline and diesel vehicles were simultaneously characterized by dynamometer testing in Guangzhou, China. The total IVOC emission factors of the diesel vehicles were approximately 16 times those of the gasoline vehicles, and IVOCs were mainly concentrated in the particle phase in the form of the unresolved complex mixture (UCM). The chemical compositions and volatility distributions of the gas-phase IVOCs differed much between gasoline and diesel vehicles, but were similar to those of their respective fuel content. This indicated that vehicle fuel is the main origin for the gas-phase IVOC emissions from vehicles. In comparison, the chemical compositions of the particle-phase IVOCs from gasoline and diesel vehicles were similar and close to lubricating oil content, implying that lubricating oil plays an important role in contributing to particle-phase IVOCs. The highest IVOC fraction in the particle phase occurred from B16-B18 volatility bins, overall accounting for more than half of the particle-phase IVOCs for both the gasoline and diesel vehicles. A conceptual model was developed to articulate the distributions of lubricating oil contents and their evaporation and nucleation/adsorption capabilities in the different volatility bins. The IVOCs-produced secondary organic aerosol (SOA) were 1.4–2.6 and 3.9–11.7 times POAs emitted from the gasoline and diesel vehicles, respectively. The tightening of emission standards had not effectively reduced IVOC emissions and the SOA production until the implementation of China VI emission standard. This underscores the importance of accelerating the promotion of the latest emission standard to alleviate pollution from vehicles in China.

Volatility Distribution of Organic Compounds in Sewage Incineration Emissions.

Intermediate volatility and semivolatile organic compounds (IVOC/SVOC) are important precursors of secondary organic aerosol (SOA) while SVOC is an important contributor to primary organic aerosol (POA). However, combustion emissions data for volatility classes are limited. This study reports the gas and particle emissions that were sampled with various dilution factors from a sewage sludge incinerator burning fuel oil. Volatility distributions were determined using measurements from online mass spectrometry and offline organic compound analyses. In the low volatility organic compound (LVOC) to IVOC range, volatility bins with organic saturation concentrations of 10-100 μg m-3 were most abundant, which was due to organic acids generated from sludge burning. Organic aerosol (OA) emission factors (EFOA) increased 1.4 times after cooling to ambient temperatures in comparison to those of the samples from the hot stack. Upon further isothermal dilution at 25 °C, the EFOA decreased while organic gas phase EFs increased with increasing dilution. Phase partitioning in volatility bins with saturation concentrations of 10-100 μg m-3 was sensitive to isothermal dilution that influenced the EFs. Therefore, gas- and particle-phase measurements alone cannot constrain EFs for these volatility classes. Low dilution factors may overestimate the particle phase and underestimate the gas phase EFs compared with real-world emission conditions.

Understanding sources of organic aerosol during CalNex-2010 using the CMAQ-VBS

Abstract. Community Multiscale Air Quality (CMAQ) model simulations utilizing the traditional organic aerosol (OA) treatment (CMAQ-AE6) and a volatility basis set (VBS) treatment for OA (CMAQ-VBS) were evaluated against measurements collected at routine monitoring networks (Chemical Speciation Network (CSN) and Interagency Monitoring of Protected Visual Environments (IMPROVE)) and those collected during the 2010 California at the Nexus of Air Quality and Climate Change (CalNex) field campaign to examine important sources of OA in southern California. Traditionally, CMAQ treats primary organic aerosol (POA) as nonvolatile and uses a two-product framework to represent secondary organic aerosol (SOA) formation. CMAQ-VBS instead treats POA as semivolatile and lumps OA using volatility bins spaced an order of magnitude apart. The CMAQ-VBS approach underpredicted organic carbon (OC) at IMPROVE and CSN sites to a greater degree than CMAQ-AE6 due to the semivolatile POA treatment. However, comparisons to aerosol mass spectrometer (AMS) measurements collected at Pasadena, CA, indicated that CMAQ-VBS better represented the diurnal profile and primary/secondary split of OA. CMAQ-VBS SOA underpredicted the average measured AMS oxygenated organic aerosol (OOA, a surrogate for SOA) concentration by a factor of 5.2, representing a considerable improvement to CMAQ-AE6 SOA predictions (factor of 24 lower than AMS). We use two new methods, one based on species ratios (SOA/ΔCO and SOA/Ox) and another on a simplified SOA parameterization, to apportion the SOA underprediction for CMAQ-VBS to slow photochemical oxidation (estimated as 1.5 × lower than observed at Pasadena using −log(NOx : NOy)), low intrinsic SOA formation efficiency (low by 1.6 to 2 × for Pasadena), and low emissions or excessive dispersion for the Pasadena site (estimated to be 1.6 to 2.3 × too low/excessive). The first and third factors are common to CMAQ-AE6, while the intrinsic SOA formation efficiency for that model is estimated to be too low by about 7 × . From source-apportioned model results, we found most of the CMAQ-VBS modeled POA at the Pasadena CalNex site was attributable to meat cooking emissions (48 %, consistent with a substantial fraction of cooking OA in the observations). This is compared to 18 % from gasoline vehicle emissions, 13 % from biomass burning (in the form of residential wood combustion), and 8 % from diesel vehicle emissions. All "other" inventoried emission sources (e.g., industrial, point, and area sources) comprised the final 13 %. The CMAQ-VBS semivolatile POA treatment underpredicted AMS hydrocarbon-like OA (HOA) + cooking-influenced OA (CIOA) at Pasadena by a factor of 1.8 compared to a factor of 1.4 overprediction of POA in CMAQ-AE6, but it did capture the AMS diurnal profile of HOA and CIOA well, with the exception of the midday peak. Overall, the CMAQ-VBS with its semivolatile treatment of POA, SOA from intermediate volatility organic compounds (IVOCs), and aging of SOA improves SOA model performance (though SOA formation efficiency is still 1.6–2 × too low). However, continued efforts are needed to better understand assumptions in the parameterization (e.g., SOA aging) and provide additional certainty to how best to apply existing emission inventories in a framework that treats POA as semivolatile, which currently degrades existing model performance at routine monitoring networks. The VBS and other approaches (e.g., AE6) require additional work to appropriately incorporate IVOC emissions and subsequent SOA formation.

Temperature dependence of source specific volatility basis sets for motor vehicle exhaust

Recent work on emissions testing has focused on developing source specific volatility distributions which could be used to improve emissions inventories. One problem about these volatility profiles is that they are evaluated only at one temperature which is usually 298 K. This study uses a simple statistical model to evaluate the temperature dependence of the source-resolved volatility basis set, considering gasoline and diesel vehicle exhaust. The steps involved (a) fitting a distribution to the emissions data (b) evaluating the goodness of fit using a statistical test (c) updating the volatility bins using the Clausius–Clayperon equation; calculating the heats of vaporization of each volatility class using a regression model (d) assessing how the volatility of different VOC classes-Extremely Low Volatile, Low Volatile, Semi-Volatile, Intermediate Volatile and Volatile Organic Compounds – are affected by temperature. The results indicated that there could be significant changes in gas-particle partitioning of these emissions. For diesel exhaust at 298 K, the fractions are 5.4 × 10−4 (ELVOC), 0.074 (LVOC), 0.76 (SVOC), 0.17 (IVOC) and 10−5 (VOC) respectively. Looking at a window of ∓20 K, the partitioning for 278 K is 3 × 10−3 (ELVOC), 0.26 (LVOC), 0.67 (SVOC), 0.07 (IVOC) with no VOC fraction; while at 318 K it is 1.5 × 10−7 (ELVOC), 9 × 10−3 (LVOC), 0.64 (SVOC), 0.35 (IVOC) and 2 × 10−5 (VOC); demonstrating a significant change with temperature. The parameterizations developed in this work could be used to improve motor vehicle emissions inventory models such as MOVES.

Phase partitioning and volatility of secondary organic aerosol components formed from α-pinene ozonolysis and OH oxidation: the importance of accretion products and other low volatility compounds

Abstract. We measured a large suite of gas- and particle-phase multi-functional organic compounds with a Filter Inlet for Gases and AEROsols (FIGAERO) coupled to a high-resolution time-of-flight chemical ionization mass spectrometer (HR-ToF-CIMS) developed at the University of Washington. The instrument was deployed on environmental simulation chambers to study monoterpene oxidation as a secondary organic aerosol (SOA) source. We focus here on results from experiments utilizing an ionization method most selective towards acids (acetate negative ion proton transfer), but our conclusions are based on more general physical and chemical properties of the SOA. Hundreds of compounds were observed in both gas and particle phases, the latter being detected by temperature-programmed thermal desorption of collected particles. Particulate organic compounds detected by the FIGAERO–HR-ToF-CIMS are highly correlated with, and explain at least 25–50 % of, the organic aerosol mass measured by an Aerodyne aerosol mass spectrometer (AMS). Reproducible multi-modal structures in the thermograms for individual compounds of a given elemental composition reveal a significant SOA mass contribution from high molecular weight organics and/or oligomers (i.e., multi-phase accretion reaction products). Approximately 50 % of the HR-ToF-CIMS particle-phase mass is associated with compounds having effective vapor pressures 4 or more orders of magnitude lower than commonly measured monoterpene oxidation products. The relative importance of these accretion-type and other extremely low volatility products appears to vary with photochemical conditions. We present a desorption-temperature-based framework for apportionment of thermogram signals into volatility bins. The volatility-based apportionment greatly improves agreement between measured and modeled gas-particle partitioning for select major and minor components of the SOA, consistent with thermal decomposition during desorption causing the conversion of lower volatility components into the detected higher volatility compounds.

Evaluations of tropospheric aerosol properties simulated by the community earth system model with a sectional aerosol microphysics scheme.

A sectional aerosol model (CARMA) has been developed and coupled with the Community Earth System Model (CESM1). Aerosol microphysics, radiative properties, and interactions with clouds are simulated in the size‐resolving model. The model described here uses 20 particle size bins for each aerosol component including freshly nucleated sulfate particles, as well as mixed particles containing sulfate, primary organics, black carbon, dust, and sea salt. The model also includes five types of bulk secondary organic aerosols with four volatility bins. The overall cost of CESM1‐CARMA is approximately ∼2.6 times as much computer time as the standard three‐mode aerosol model in CESM1 (CESM1‐MAM3) and twice as much computer time as the seven‐mode aerosol model in CESM1 (CESM1‐MAM7) using similar gas phase chemistry codes. Aerosol spatial‐temporal distributions are simulated and compared with a large set of observations from satellites, ground‐based measurements, and airborne field campaigns. Simulated annual average aerosol optical depths are lower than MODIS/MISR satellite observations and AERONET observations by ∼32%. This difference is within the uncertainty of the satellite observations. CESM1/CARMA reproduces sulfate aerosol mass within 8%, organic aerosol mass within 20%, and black carbon aerosol mass within 50% compared with a multiyear average of the IMPROVE/EPA data over United States, but differences vary considerably at individual locations. Other data sets show similar levels of comparison with model simulations. The model suggests that in addition to sulfate, organic aerosols also significantly contribute to aerosol mass in the tropical UTLS, which is consistent with limited data.

1.5-Dimensional volatility basis set approach for modeling organic aerosol in CAMx and CMAQ

A hybrid volatility basis set (VBS) approach to modeling atmospheric organic aerosol (OA) is developed that combines the simplicity of the 1-dimensional (1-D) VBS with the ability to describe evolution of OA in the 2-dimensional space of oxidation state and volatility. This 1.5-D scheme uses four basis sets to describe varying degrees of oxidation in ambient OA: two basis sets for chemically aged oxygenated OA (anthropogenic and biogenic) and two for freshly emitted OA (from anthropogenic sources and biomass burning). Each basis set has five volatility bins including a zero-volatility bin for essentially non-volatile compounds. The scheme adjusts oxidation state as well as volatility in response to chemical aging by simplifying the 2-dimensional VBS model. The 1.5-D VBS module is implemented in two widely used photochemical grid models (CAMx and CMAQ) and evaluated for summer and winter 2005 episodes over the eastern U.S. CAMx performs reasonably well in predicting observed organic carbon (OC) concentrations while CMAQ under-estimates OC, with differences between models being attributed to science algorithms other than the VBS. Oxygenated OA accounts for less than half of the modeled OA mass in winter but about 80% of total OA in summer due to more rapid chemical aging in summer.

Sources and production of organic aerosol in Mexico City: insights from the combination of a chemical transport model (PMCAMx-2008) and measurements during MILAGRO

Abstract. Urban areas are large sources of organic aerosols and their precursors. Nevertheless, the contributions of primary (POA) and secondary organic aerosol (SOA) to the observed particulate matter levels have been difficult to quantify. In this study the three-dimensional chemical transport model PMCAMx-2008 is used to investigate the temporal and geographic variability of organic aerosol in the Mexico City Metropolitan Area (MCMA) during the MILAGRO campaign that took place in the spring of 2006. The organic module of PMCAMx-2008 includes the recently developed volatility basis-set framework in which both primary and secondary organic components are assumed to be semi-volatile and photochemically reactive and are distributed in logarithmically spaced volatility bins. The MCMA emission inventory is modified and the POA emissions are distributed by volatility based on dilution experiments. The model predictions are compared with observations from four different types of sites, an urban (T0), a suburban (T1), a rural (T2), and an elevated site in Pico de Tres Padres (PTP). The performance of the model in reproducing organic mass concentrations in these sites is encouraging. The average predicted PM1 organic aerosol (OA) concentration in T0, T1, and T2 is 18 μg m−3, 11.7 μg m−3, and 10.5 μg m−3 respectively, while the corresponding measured values are 17.2 μg m−3, 11 μg m−3, and 9 μg m−3. The average predicted locally-emitted primary OA concentrations, 4.4 μg m−3 at T0, 1.2 μg m−3 at T1 and 1.7 μg m−3 at PTP, are in reasonably good agreement with the corresponding PMF analysis estimates based on the Aerosol Mass Spectrometer (AMS) observations of 4.5, 1.3, and 2.9 μg m−3 respectively. The model reproduces reasonably well the average oxygenated OA (OOA) levels in T0 (7.5 μg m−3 predicted versus 7.5 μg m−3 measured), in T1 (6.3 μg m−3 predicted versus 4.6 μg m−3 measured) and in PTP (6.6 μg m−3 predicted versus 5.9 μg m−3 measured). The rest of the OA mass (6.1 μg m−3 and 4.2 μg m−3 in T0 and T1 respectively) is assumed to originate from biomass burning activities and is introduced to the model as part of the boundary conditions. Inside Mexico City (at T0), the locally-produced OA is predicted to be on average 60 % locally-emitted primary (POA), 6 % semi-volatile (S-SOA) and intermediate volatile (I-SOA) organic aerosol, and 34 % traditional SOA from the oxidation of VOCs (V-SOA). The average contributions of the OA components to the locally-produced OA for the entire modelling domain are predicted to be 32 % POA, 10 % S-SOA and I-SOA, and 58 % V-SOA. The long range transport from biomass burning activities and other sources in Mexico is predicted to contribute on average almost as much as the local sources during the MILAGRO period.

Simulations of organic aerosol concentrations in Mexico City using the WRF-CHEM model during the MCMA-2006/MILAGRO campaign

Abstract. Organic aerosol concentrations are simulated using the WRF-CHEM model in Mexico City during the period from 24 to 29 March in association with the MILAGRO-2006 campaign. Two approaches are employed to predict the variation and spatial distribution of the organic aerosol concentrations: (1) a traditional 2-product secondary organic aerosol (SOA) model with non-volatile primary organic aerosols (POA); (2) a non-traditional SOA model including the volatility basis-set modeling method in which primary organic components are assumed to be semi-volatile and photochemically reactive and are distributed in logarithmically spaced volatility bins. The MCMA (Mexico City Metropolitan Area) 2006 official emission inventory is used in simulations and the POA emissions are modified and distributed by volatility based on dilution experiments for the non-traditional SOA model. The model results are compared to the Aerosol Mass Spectrometry (AMS) observations analyzed using the Positive Matrix Factorization (PMF) technique at an urban background site (T0) and a suburban background site (T1) in Mexico City. The traditional SOA model frequently underestimates the observed POA concentrations during rush hours and overestimates the observations in the rest of the time in the city. The model also substantially underestimates the observed SOA concentrations, particularly during daytime, and only produces 21% and 25% of the observed SOA mass in the suburban and urban area, respectively. The non-traditional SOA model performs well in simulating the POA variation, but still overestimates during daytime in the urban area. The SOA simulations are significantly improved in the non-traditional SOA model compared to the traditional SOA model and the SOA production is increased by more than 100% in the city. However, the underestimation during daytime is still salient in the urban area and the non-traditional model also fails to reproduce the high level of SOA concentrations in the suburban area. In the non-traditional SOA model, the aging process of primary organic components considerably decreases the OH levels in simulations and further impacts the SOA formation. If the aging process in the non-traditional model does not have feedback on the OH in the gas-phase chemistry, the SOA production is enhanced by more than 10% compared to the simulations with the OH feedback during daytime, and the gap between the simulations and observations in the urban area is around 3 μg m−3 or 20% on average during late morning and early afternoon, within the uncertainty from the AMS measurements and PMF analysis. In addition, glyoxal and methylglyoxal can contribute up to approximately 10% of the observed SOA mass in the urban area and 4% in the suburban area. Including the non-OH feedback and the contribution of glyoxal and methylglyoxal, the non-traditional SOA model can explain up to 83% of the observed SOA in the urban area, and the underestimation during late morning and early afternoon is reduced to 0.9 μg m−3 or 6% on average. Considering the uncertainties from measurements, emissions, meteorological conditions, aging of semi-volatile and intermediate volatile organic compounds, and contributions from background transport, the non-traditional SOA model is capable of closing the gap in SOA mass between measurements and models.

Evaluation of the volatility basis-set approach for the simulation of organic aerosol formation in the Mexico City metropolitan area

Abstract. New primary and secondary organic aerosol modules have been added to PMCAMx, a three dimensional chemical transport model (CTM), for use with the SAPRC99 chemistry mechanism based on recent smog chamber studies. The new modelling framework is based on the volatility basis-set approach: both primary and secondary organic components are assumed to be semivolatile and photochemically reactive and are distributed in logarithmically spaced volatility bins. This new framework with the use of the new volatility basis parameters for low-NOx and high-NOx conditions tends to predict 4–6 times higher anthropogenic SOA concentrations than those predicted with the older generation of models. The resulting PMCAMx-2008 was applied in Mexico City Metropolitan Area (MCMA) for approximately a week during April 2003 during a period of very low regional biomass burning impact. The emission inventory, which uses as a starting point the MCMA 2004 official inventory, is modified and the primary organic aerosol (POA) emissions are distributed by volatility based on dilution experiments. The predicted organic aerosol (OA) concentrations peak in the center of Mexico City, reaching values above 40 μg m−3. The model predictions are compared with the results of the Positive Matrix Factorization (PMF) analysis of the Aerosol Mass Spectrometry (AMS) observations. The model reproduces both Hydrocarbon-like Organic Aerosol (HOA) and Oxygenated Organic Aerosol (OOA) concentrations and diurnal profiles. The small OA underprediction during the rush-hour periods and overprediction in the afternoon suggest potential improvements to the description of fresh primary organic emissions and the formation of the oxygenated organic aerosols, respectively, although they may also be due to errors in the simulation of dispersion and vertical mixing. However, the AMS OOA data are not specific enough to prove that the model reproduces the organic aerosol observations for the right reasons. Other combinations of contributions of primary and secondary organic aerosol production rates may lead to similar results. The model results strongly suggest that, during the simulated period, transport of OA from outside the city was a significant contributor to the observed OA levels. Future simulations should use a larger domain in order to test whether the regional OA can be predicted with current SOA parameterizations. Sensitivity tests indicate that the predicted OA concentration is especially sensitive to the volatility distribution of the emissions in the lower volatility bins.

Modeling of secondary organic aerosol yields from laboratory chamber data

Abstract. Laboratory chamber data serve as the basis for constraining models of secondary organic aerosol (SOA) formation. Current models fall into three categories: empirical two-product (Odum), product-specific, and volatility basis set. The product-specific and volatility basis set models are applied here to represent laboratory data on the ozonolysis of α-pinene under dry, dark, and low-NOx conditions in the presence of ammonium sulfate seed aerosol. Using five major identified products, the model is fit to the chamber data. From the optimal fitting, SOA oxygen-to-carbon (O/C) and hydrogen-to-carbon (H/C) ratios are modeled. The discrepancy between measured H/C ratios and those based on the oxidation products used in the model fitting suggests the potential importance of particle-phase reactions. Data fitting is also carried out using the volatility basis set, wherein oxidation products are parsed into volatility bins. The product-specific model is most likely hindered by lack of explicit inclusion of particle-phase accretion compounds. While prospects for identification of the majority of SOA products for major volatile organic compounds (VOCs) classes remain promising, for the near future empirical product or volatility basis set models remain the approaches of choice.