Changes in organic aerosol composition with aging inferred from aerosol mass spectra

Organic aerosols (OA) can be separated with factor analysis of aerosol mass spectrometer (AMS) data into hydrocarbon-like OA (HOA) and oxygenated OA (OOA). We develop a new method to parameterize H:C of OOA in terms of f_(43)(ratio of m/z 43, mostly C_2H_3O^+, to total signal in the component mass spectrum). Such parameterization allows for the transformation of large database of ambient OOA components from the f_(44) (mostly CO^+_2, likely from acid groups) vs. f_(43) space ("triangle plot") (Ng et al., 2010) into the Van Krevelen diagram (H:C vs. O:C) (Van Krevelen, 1950). Heald et al. (2010) examined the evolution of total OA in the Van Krevelen diagram. In this work total OA is deconvolved into components that correspond to primary (HOA and others) and secondary (OOA) organic aerosols. By deconvolving total OA into different components, we remove physical mixing effects between secondary and primary aerosols which allows for examination of the evolution of OOA components alone in the Van Krevelen space. This provides a unique means of following ambient secondary OA evolution that is analogous to and can be compared with trends observed in chamber studies of secondary organic aerosol formation. The triangle plot in Ng et al. (2010) indicates that f_(44) of OOA components increases with photochemical age, suggesting the importance of acid formation in OOA evolution. Once they are transformed with the new parameterization, the triangle plot of the OOA components from all sites occupy an area in Van Krevelen space which follows a ΔH:C/ΔO:C slope of ~ −0.5. This slope suggests that ambient OOA aging results in net changes in chemical composition that are equivalent to the addition of both acid and alcohol/peroxide functional groups without fragmentation (i.e. C-C bond breakage), and/or the addition of acid groups with fragmentation. These results provide a framework for linking the bulk aerosol chemical composition evolution to molecular-level studies

Hydrocarbon-like and oxygenated organic aerosols in Pittsburgh: insights into sources and processes of organic aerosols

International audienceA recently developed algorithm (Zhang et al., 2005) has been applied to deconvolve the mass spectra of organic aerosols acquired with the Aerosol Mass Spectrometer (AMS) in Pittsburgh during September 2002. The results are used here to characterize the mass concentrations, size distributions, and mass spectra of hydrocarbon-like and oxygenated organic aerosol (HOA and OOA, respectively). HOA accounts for 34% of the measured organic aerosol mass and OOA accounts for 66%. The mass concentrations of HOA demonstrate a prominent diurnal profile that peaks in the morning during the rush hour and decreases with the rise of the boundary layer. The diurnal profile of OOA is relatively flat and resembles those of SO42? and NH4+. The size distribution of HOA shows a distinct ultrafine mode that is commonly associated with fresh emissions while OOA is generally concentrated in the accumulation mode and appears to be mostly internally mixed with the inorganic ions, such as SO42? and NH4+. These observations suggest that HOA is likely primary aerosol from local, combustion-related emissions and that OOA is secondary organic aerosol (SOA) influenced by regional contributions. There is strong evidence of the direct correspondence of OOA to SOA during an intense new particle formation and growth event, when condensational growth of OOA was observed. The fact that the OOA mass spectrum from this event is very similar to that from the entire study suggests that the majority of OOA in Pittsburgh is likely SOA. O3 appears to be a poor indicator for OOA concentration while SO42? is a relatively good surrogate for this dataset. Since the diurnal averages of HOA track those of CO during day time, oxidation/aging of HOA appears to be very small on the time scale of several hours. Based on extracted mass spectra and the likely elemental compositions of major m/z's, the organic mass to organic carbon ratios (OM:OC) of HOA and OOA are estimated at 1.2 and 2.2 ?g/?gC, respectively, leading to an average OM:OC ratio of 1.8 for submicron OA in Pittsburgh during September. The C:O ratio of OOA is estimated at 1:0.8. The carbon contents in HOA and OOA estimated accordingly correlate well to primary and secondary organic carbon, respectively, estimated by the OC/EC tracer technique (assuming POC-to-EC ratio=1). In addition, the total carbon concentrations estimated from the AMS data agree well with those measured by the Sunset Laboratory Carbon analyzer (r2=0.87; slope=1.01±0.11). Our results represent the first direct estimate of the OM:OC ratio from highly time-resolved chemical composition measurements

Elemental composition and oxidation of chamber organic aerosol

Recently, graphical representations of aerosol mass spectrometer (AMS) spectra and elemental composition have been developed to explain the oxidative and aging processes of secondary organic aerosol (SOA). It has been shown previously that oxygenated organic aerosol (OOA) components from ambient and laboratory data fall within a triangular region in the f_(44) vs. f_(43) space, where f_(44) and f_(43) are the ratios of the organic signal at m/z 44 and 43 to the total organic signal in AMS spectra, respectively; we refer to this graphical representation as the "triangle plot." Alternatively, the Van Krevelen diagram has been used to describe the evolution of functional groups in SOA. In this study we investigate the variability of SOA formed in chamber experiments from twelve different precursors in both "triangle plot" and Van Krevelen domains. Spectral and elemental data from the high-resolution Aerodyne aerosol mass spectrometer are compared to offline species identification analysis and FTIR filter analysis to better understand the changes in functional and elemental composition inherent in SOA formation and aging. We find that SOA formed under high- and low-NO_x conditions occupy similar areas in the "triangle plot" and Van Krevelen diagram and that SOA generated from already oxidized precursors allows for the exploration of areas higher on the "triangle plot" not easily accessible with non-oxidized precursors. As SOA ages, it migrates toward the top of the triangle along a path largely dependent on the precursor identity, which suggests increasing organic acid content and decreasing mass spectral variability. The most oxidized SOA come from the photooxidation of methoxyphenol precursors which yielded SOA O/C ratios near unity. α-pinene ozonolysis and naphthalene photooxidation SOA systems have had the highest degree of mass closure in previous chemical characterization studies and also show the best agreement between AMS elemental composition measurements and elemental composition of identified species within the uncertainty of the AMS elemental analysis. In general, compared to their respective unsaturated SOA precursors, the elemental composition of chamber SOA follows a slope shallower than −1 on the Van Krevelen diagram, which is indicative of oxidation of the precursor without substantial losss of hydrogen, likely due to the unsaturated nature of the precursors. From the spectra of SOA studied here, we are able to reproduce the triangular region originally constructed with ambient OOA compents with chamber aerosol showing that SOA becomes more chemically similar as it ages. Ambient data in the middle of the triangle represent the ensemble average of many different SOA precursors, ages, and oxidative processes

Interpretation of organic components from positive matrix factorization of aerosol mass spectrometric data

International audienceThe organic aerosol (OA) dataset from an Aerodyne Aerosol Mass Spectrometer (Q-AMS) collected at the Pittsburgh Air Quality Study in September 2002 was analyzed for components with Positive Matrix Factorization (PMF). Three components ? hydrocarbon-like organic aerosol OA (HOA), a highly-oxygenated OA (OOA-I) that correlates well with sulfate, and a less-oxygenated, semi-volatile OA (OOA-II) that correlates well with nitrate and chloride ? are identified and interpreted as primary combustion emissions, aged SOA, and semivolatile, less aged SOA, respectively. The complexity of interpreting the PMF solutions of unit mass resolution (UMR) AMS data is illustrated by a detailed analysis of the solutions as a function of number of components and rotational state. A public database of AMS spectra has been created to aid this type of analysis. A sensitivity analysis with realistic synthetic data is also used to characterize the behavior of PMF for choosing the best number of factors, rotations of non-unique solutions, and the retrievability of more (or less) correlated factors. The ambient and synthetic data indicate that the variation of the PMF quality of fit parameter (Q, a normalized chi-squared metric) vs. number of factors in the solution is useful to identify the minimum number of factors, but more detailed analysis and interpretation is needed to choose the best number of factors. The maximum value of the rotational matrix is not useful for determining the best number of factors. In synthetic datasets, factors are "split" into two or more components when solving for more factors than were used in the input. Elements of the "splitting" behavior are observed in solutions of real datasets with several factors. Significant structure remains in the residual of the real dataset after physically-meaningful factors have been assigned and an unrealistic number of factors would be required to explain the remaining variance. This residual structure appears to be due to variability in the spectra of the components (especially OOA-II in this case), which is likely to be a key limit of the retrievability of components from AMS datasets using PMF and similar methods that need to assume constant component mass spectra. Methods for characterizing and dealing with this variability are needed. Values of the rotational parameter (FPEAK) near zero appear to be most appropriate for these datasets. Interpretation of PMF factors must be done carefully. Synthetic data indicate that PMF internal diagnostics and similarity to available source component spectra together are not sufficient for identifying factors. It is critical to use correlations between factor time series and external measurement time series to support factor interpretations. Components with R>0.9) with other components are suspect and should be interpreted with care. Results from this study may be useful for interpreting the PMF analysis of data from other aerosol mass spectrometers

Modeling organic aerosols in a megacity: potential contribution of semi-volatile and intermediate volatility primary organic compounds to secondary organic aerosol formation

It has been established that observed local and regional levels of secondary organic aerosols (SOA) in polluted areas cannot be explained by the oxidation and partitioning of anthropogenic and biogenic VOC precursors, at least using current mechanisms and parameterizations. In this study, the 3-D regional air quality model CHIMERE is applied to estimate the potential contribution to SOA formation of recently identified semi-volatile and intermediate volatility organic precursors (S/IVOC) in and around Mexico City for the MILAGRO field experiment during March 2006. The model has been updated to include explicitly the volatility distribution of primary organic aerosols (POA), their gas-particle partitioning and the gas-phase oxidation of the vapors. Two recently proposed parameterizations, those of Robinson et al. (2007) ("ROB") and Grieshop et al. (2009) ("GRI") are compared and evaluated against surface and aircraft measurements. The 3-D model results are assessed by comparing with the concentrations of OA components from Positive Matrix Factorization of Aerosol Mass Spectrometer (AMS) data, and for the first time also with oxygen-to-carbon ratios derived from high-resolution AMS measurements. The results show a substantial enhancement in predicted SOA concentrations (2–4 times) with respect to the previously published base case without S/IVOCs (Hodzic et al., 2009), both within and downwind of the city leading to much reduced discrepancies with the total OA measurements. Model improvements in OA predictions are associated with the better-captured SOA magnitude and diurnal variability. The predicted production from anthropogenic and biomass burning S/IVOC represents 40–60% of the total measured SOA at the surface during the day and is somewhat larger than that from commonly measured aromatic VOCs, especially at the T1 site at the edge of the city. The SOA production from the continued multi-generation S/IVOC oxidation products continues actively downwind. Similar to aircraft observations, the predicted OA/ΔCO ratio for the ROB case increases from 20–30 μg sm<sup>−3</sup> ppm<sup>−1</sup> up to 60–70 μg sm<sup>−3</sup> ppm<sup>−1</sup> between a fresh and 1-day aged air mass, while the GRI case produces a 30% higher OA growth than observed. The predicted average O/C ratio of total OA for the ROB case is 0.16 at T0, substantially below observed value of 0.5. A much better agreement for O/C ratios and temporal variability (<i>R</i><sup>2</sup>=0.63) is achieved with the updated GRI treatment. Both treatments show a deficiency in regard to POA ageing with a tendency to over-evaporate POA upon dilution of the urban plume suggesting that atmospheric HOA may be less volatile than assumed in these parameterizations. This study highlights the important potential role of S/IVOC chemistry in the SOA budget in this region, and highlights the need for further improvements in available parameterizations. The agreement observed in this study is not sufficient evidence to conclude that S/IVOC are the major missing SOA source in megacity environments. The model is still very underconstrained, and other possible pathways such as formation from very volatile species like glyoxal may explain some of the mass and especially increase the O/C ratio

Deconvolution of FIGAERO-CIMS thermal desorption profiles using positive matrix factorisation to identify chemical and physical processes during particle evaporation

he measurements of aerosol particles with a filter inlet for gases and aerosols (FIGAERO) together with a chemical ionisation mass spectrometer (CIMS) yield the overall chemical composition of the particle phase. In addition, the thermal desorption profiles obtained for each detected ion composition contain information about the volatility of the detected compounds, which is an important property for understanding many physical properties like gas–particle partitioning. We coupled this thermal desorption method with isothermal evaporation prior to the sample collection to investigate the chemical composition changes during isothermal particle evaporation and particulate-water-driven chemical reactions in α-pinene secondary organic aerosol (SOA) of three different oxidative states. The thermal desorption profiles of all detected elemental compositions were then analysed with positive matrix factorisation (PMF) to identify the drivers of the chemical composition changes observed during isothermal evaporation. The keys to this analysis were to use the error matrix as a tool to weight the parts of the data carrying most information (i.e. the peak area of each thermogram) and to run PMF on a combined data set of multiple thermograms from different experiments to enable a direct comparison of the individual factors between separate measurements. The PMF was able to identify instrument background factors and separate them from the part of the data containing particle desorption information. Additionally, PMF allowed us to separate the direct desorption of compounds detected at a specific elemental composition from other signals with the same composition that stem from the thermal decomposition of thermally instable compounds with lower volatility. For each SOA type, 7–9 factors were needed to explain the observed thermogram behaviour. The contribution of the factors depended on the prior isothermal evaporation. Decreased contributions from the factors with the lowest desorption temperatures were observed with increasing isothermal evaporation time. Thus, the factors identified by PMF could be interpreted as volatility classes. The composition changes in the particles due to isothermal evaporation could be attributed to the removal of volatile factors with very little change in the desorption profiles of the individual factors (i.e. in the respective temperatures of peak desorption, Tmax). When aqueous-phase reactions took place, PMF was able to identify a new factor that directly identified the ions affected by the chemical processes. We conducted a PMF analysis of the FIGAERO–CIMS thermal desorption data for the first time using laboratory-generated SOA particles. But this method can be applied to, for example, ambient FIGAERO–CIMS measurements as well. There, the PMF analysis of the thermal desorption data identifies organic aerosol (OA) sources (such as biomass burning or oxidation of different precursors) and types, e.g. hydrocarbon-like (HOA) or oxygenated organic aerosol (OOA). This information could also be obtained with the traditional approach, namely the PMF analysis of the mass spectra data integrated for each thermogram. But only our method can also obtain the volatility information for each OA source and type. Additionally, we can identify the contribution of thermal decomposition to the overall signal

Relating hygroscopicity and composition of organic aerosol particulate matter

A hygroscopicity tandem differential mobility analyzer (HTDMA) was used to measure the water uptake (hygroscopicity) of secondary organic aerosol (SOA) formed during the chemical and photochemical oxidation of several organic precursors in a smog chamber. Electron ionization mass spectra of the non-refractory submicron aerosol were simultaneously determined with an aerosol mass spectrometer (AMS), and correlations between the two different signals were investigated. SOA hygroscopicity was found to strongly correlate with the relative abundance of the ion signal m/z 44 expressed as a fraction of total organic signal (f44). m/z 44 is due mostly to the ion fragment CO2+ for all types of SOA systems studied, and has been previously shown to strongly correlate with organic O/C for ambient and chamber OA. The analysis was also performed on ambient OA from two field experiments at the remote site Jungfraujoch, and the megacity Mexico City, where similar results were found. A simple empirical linear relation between the hygroscopicity of OA at subsaturated RH, as given by the hygroscopic growth factor (GF) or "ϰorg" parameter, and f44 was determined and is given by ϰorg = 2.2 × f44 − 0.13. This approximation can be further verified and refined as the database for AMS and HTDMA measurements is constantly being expanded around the world. The use of this approximation could introduce an important simplification in the parameterization of hygroscopicity of OA in atmospheric models, since f44 is correlated with the photochemical age of an air mass

Relating hygroscopicity and composition of organic aerosol particulate matter

A hygroscopicity tandem differential mobility analyzer (HTDMA) was used to measure the water uptake (hygroscopicity) of secondary organic aerosol (SOA) formed during the chemical and photochemical oxidation of several organic precursors in a smog chamber. Electron ionization mass spectra of the non-refractory submicron aerosol were simultaneously determined with an aerosol mass spectrometer (AMS), and correlations between the two different signals were investigated. SOA hygroscopicity was found to strongly correlate with the relative abundance of the ion signal m/z 44 expressed as a fraction of total organic signal (f44). m/z 44 is due mostly to the ion fragment CO2+ for all types of SOA systems studied, and has been previously shown to strongly correlate with organic O/C for ambient and chamber OA. The analysis was also performed on ambient OA from two field experiments at the remote site Jungfraujoch, and the megacity Mexico City, where similar results were found. A simple empirical linear relation between the hygroscopicity of OA at subsaturated RH, as given by the hygroscopic growth factor (GF) or "κorg" parameter, and f44 was determined and is given by κorg=2.2×f44−0.13. This approximation can be further verified and refined as the database for AMS and HTDMA measurements is constantly being expanded around the world. The use of this approximation could introduce an important simplification in the parameterization of hygroscopicity of OA in atmospheric models, since f44 is correlated with the photochemical age of an air mass

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

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[superscript −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[superscript −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.National Science Foundation (U.S.). Atmospheric Chemistry Program (ATM-0528227)National Science Foundation (U.S.). Atmospheric Chemistry Program (ATM-0810931)Molina Center for Energy and the Environmen