Organic aerosol (OA) data acquired by the Aerosol Mass Spectrometer (AMS) in 37 field campaigns were deconvolved into hydrocarbon‐like OA (HOA) and several types of oxygenated OA (OOA) components. HOA has been linked to primary combustion emissions (mainly from fossil fuel) and other primary sources such as meat cooking. OOA is ubiquitous in various atmospheric environments, on average accounting for 64%, 83% and 95% of the total OA in urban, urban downwind, and rural/remote sites, respectively. A case study analysis of a rural site shows that the OOA concentration is much greater than the advected HOA, indicating that HOA oxidation is not an important source of OOA, and that OOA increases are mainly due to SOA. Most global models lack an explicit representation of SOA which may lead to significant biases in the magnitude, spatial and temporal distributions of OA, and in aerosol hygroscopic properties.

Organic aerosol (OA) in the atmosphere consists of a multitude of organic species which are either directly emitted or the products of a variety of chemical reactions. This complexity challenges our ability to explicitly characterize the chemical composition of these particles. We find that the bulk composition of OA from a variety of environments (laboratory and field) occupies a narrow range in the space of a Van Krevelen diagram (H:C versus O:C), characterized by a slope of ∼−1. The data show that atmospheric aging, involving processes such as volatilization, oxidation, mixing of air masses or condensation of further products, is consistent with movement along this line, producing a more oxidized aerosol. This finding has implications for our understanding of the evolution of atmospheric OA and representation of these processes in models.

Abstract. The volatilities of different chemical species in ambient aerosols are important but remain poorly characterized. The coupling of a recently developed rapid temperature-stepping thermodenuder (TD, operated in the range 54–230°C) with a High-Resolution Time-of-Flight Aerosol Mass Spectrometer (HR-ToF-AMS) during field studies in two polluted megacities has enabled the first direct characterization of chemically-resolved urban particle volatility. Measurements in Riverside, CA and Mexico City are generally consistent and show ambient nitrate as having the highest volatility of any AMS aerosol species while sulfate showed the lowest volatility. Total organic aerosol (OA) showed volatility intermediate between nitrate and sulfate, with an evaporation rate of 0.6% K−1 near ambient temperature, although OA dominates the residual species at the highest temperatures. Different types of OA were characterized with marker ions, diurnal cycles, and positive matrix factorization (PMF) and show significant differences in volatility. Reduced hydrocarbon-like OA (HOA, a surrogate for primary OA, POA), oxygenated OA (OOA, a surrogate for secondary OA, SOA), and biomass-burning OA (BBOA) separated with PMF were all determined to be semi-volatile. The most aged OOA-1 and its dominant ion, CO2+, consistently exhibited the lowest volatility, with HOA, BBOA, and associated ions for each among the highest. The similar or higher volatility of HOA/POA compared to OOA/SOA contradicts the current representations of OA volatility in most atmospheric models and has important implications for aerosol growth and lifetime. Our results strongly imply that all OA types should be considered semivolatile in models. The study in Riverside identified organosulfur species (e.g. CH3HSO3+ ion, likely from methanesulfonic acid), while both studies identified ions indicative of amines (e.g. C5H12N+) with very different volatility behaviors than inorganic-dominated ions. The oxygen-to-carbon ratio of OA in each ambient study was shown to increase both with TD temperature and from morning to afternoon, while the hydrogen-to-carbon ratio showed the opposite trend.

Abstract.The relationship between cloud condensation nuclei (CCN) number and the physical and chemical properties of the atmospheric aerosol distribution is explored for a polluted urban data set from the Study of Organic Aerosols at Riverside I (SOAR-1) campaign conducted at Riverside, California, USA during summer 2005. The mixing state and, to a lesser degree, the average chemical composition are shown to be important parameters in determining the activation properties of those particles around the critical activation diameters for atmospherically-realistic supersaturation values. Closure between predictions and measurements of CCN number at several supersaturations is attempted by modeling a number of aerosol chemical composition and mixing state cases of increasing complexity. It is shown that a realistic treatment of the state of mixing of the urban aerosol distribution is critical in order to eliminate model bias. Fresh emissions such as elemental carbon and small organic particles must be treated as non-activating and explicitly accounted for in the model. The relative number concentration of these particles compared to inorganics and oxygenated organic compounds of limited hygroscopicity plays an important role in determining the CCN number. Furthermore, expanding the different composition/mixing state cases to predictions of cloud droplet number concentration in a cloud parcel model highlights the dependence of cloud optical properties on the state of mixing and hygroscopic properties of the different Correspondence to: J. L. Jimenez (jose.jimenez@colorado.edu) aerosol modes, but shows that the relative differences between the different cases are reduced compared to those from the CCN model.

Abstract. Submicron aerosol was analyzed during the MILAGRO field campaign in March 2006 at the T0 urban supersite in Mexico City with a High-Resolution Time-of-Flight Aerosol Mass Spectrometer (HR-ToF-AMS) and complementary instrumentation. Mass concentrations, diurnal cycles, and size distributions of inorganic and organic species are similar to results from the CENICA supersite in April 2003 with organic aerosol (OA) comprising about half of the fine PM mass. Positive Matrix Factorization (PMF) analysis of the high resolution OA spectra identified three major components: chemically-reduced urban primary emissions (hydrocarbon-like OA, HOA), oxygenated OA (OOA, mostly secondary OA or SOA), and biomass burning OA (BBOA) that correlates with levoglucosan and acetonitrile. BBOA includes several very large plumes from regional fires and likely also some refuse burning. A fourth OA component is a small local nitrogen-containing reduced OA component (LOA) which accounts for 9% of the OA mass but one third of the organic nitrogen, likely as amines. OOA accounts for almost half of the OA on average, consistent with previous observations. OA apportionment results from PMF-AMS are compared to the PM2.5 chemical mass balance of organic molecular markers (CMB-OMM, from GC/MS analysis of filters). Results from both methods are overall consistent. Both assign the major components of OA to primary urban, biomass burning/woodsmoke, and secondary sources at similar magnitudes. The 2006 Mexico City emissions inventory underestimates the urban primary PM2.5 emissions by a factor of ~4, and it is ~16 times lower than afternoon concentrations when secondary species are included. Additionally, the forest fire contribution is underestimated by at least an order-of-magnitude in the inventory.

While automated techniques exist for the integration of individual gas chromatograph peaks, manual inspection of integration quality and peak choice is still required due to drifting retention times and changing peak shapes near detection limits. The feasibility of a simplified method to obtain multiple bulk species classes from complex gas chromatography data is investigated here with data from the thermal desorption aerosol gas chromatograph (TAG). Chromatograms were divided into many "chromatography bins" containing total eluting mass spectra (both from resolved species and unresolved complex mixture [UCM]), instead of only integrating resolved peaks as is performed in the traditional chromatography analysis method. Positive matrix factorization (PMF) was applied to the mass spectra of the chromatography bins to determine major factors contributing to the observed chemical composition. PMF factors are not highly sensitive to the specific PMF error estimation method applied. Increasing the number of chromatography bins that each chromatogram was divided into improved PMF results until reaching 400 bins. Increasing the number of bins above 400 does not significantly improve the PMF results. This is likely due to 400 bin separation providing bin widths (4.6 s) that match the narrowest peak widths (4.8 s) of compounds found in the TAG chromatograms. The bin-based method took only a small fraction of the time to complete compared to peak-integrated method, significantly saving operator time and effort. Finally, high-factor solutions (e.g., 20 factors) of bin-based PMF can separate many individual compounds, homologues compound series, and UCM from chromatography data.

Abstract. The relationship between cloud condensation nuclei (CCN) number and the physical and chemical properties of the atmospheric aerosol distribution is explored for a polluted urban data set from the Study of Organic Aerosols at Riverside I (SOAR-1) campaign conducted at Riverside, California, USA during summer 2005. The mixing state and, to a lesser degree, the average chemical composition are shown to be important parameters in determining the activation properties of those particles around the critical activation diameters for atmospherically-realistic supersaturation values. Closure between predictions and measurements of CCN number at several supersaturations is attempted by modeling a number of aerosol chemical composition and mixing state schemes of increasing complexity. It is shown that a realistic treatment of the state of mixing of the urban aerosol distribution is critical in order to eliminate model bias. Fresh emissions such as elemental carbon and small organic particles must be treated as non-activating and explicitly accounted for in the model scheme. The relative number concentration of these particles compared to inorganics and oxygenated organic compounds of limited hygroscopicity plays an important role in determining the CCN number. Furthermore, expanding the different composition/mixing state schemes to predictions of cloud droplet number concentration in a cloud parcel model highlights the dependence of cloud optical properties on the state of mixing and hygroscopic properties of the different aerosol modes, but shows that the relative differences between the different schemes are reduced compared to those from the CCN model.