11 research outputs found
Understanding atmospheric organic aerosols via factor analysis of aerosol mass spectrometry: a review
Organic species are an important but poorly characterized constituent of airborne particulate matter. A quantitative understanding of the organic fraction of particles (organic aerosol, OA) is necessary to reduce some of the largest uncertainties that confound the assessment of the radiative forcing of climate and air quality management policies. In recent years, aerosol mass spectrometry has been increasingly relied upon for highly time-resolved characterization of OA chemistry and for elucidation of aerosol sources and lifecycle processes. Aerodyne aerosol mass spectrometers (AMS) are particularly widely used, because of their ability to quantitatively characterize the size-resolved composition of submicron particles (PM1). AMS report the bulk composition and temporal variations of OA in the form of ensemble mass spectra (MS) acquired over short time intervals. Because each MS represents the linear superposition of the spectra of individual components weighed by their concentrations, multivariate factor analysis of the MS matrix has proved effective at retrieving OA factors that offer a quantitative and simplified description of the thousands of individual organic species. The sum of the factors accounts for nearly 100% of the OA mass and each individual factor typically corresponds to a large group of OA constituents with similar chemical composition and temporal behavior that are characteristic of different sources and/or atmospheric processes. The application of this technique in aerosol mass spectrometry has grown rapidly in the last six years. Here we review multivariate factor analysis techniques applied to AMS and other aerosol mass spectrometers, and summarize key findings from field observations. Results that provide valuable information about aerosol sources and, in particular, secondary OA evolution on regional and global scales are highlighted. Advanced methods, for example a-priori constraints on factor mass spectra and the application of factor analysis to combined aerosol and gas phase data are discussed. Integrated analysis of worldwide OA factors is used to present a holistic regional and global description of OA. Finally, different ways in which OA factors can constrain global and regional models are discussed
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Global distribution of particle phase state in atmospheric secondary organic aerosols.
Shiraiwa M, Li Y, Tsimpidi AP, et al. Global distribution of particle phase state in atmospheric secondary organic aerosols. Nat Commun. 2017;8(1): 15002
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Predictions of diffusion rates of large organic molecules in secondary organic aerosols using the Stokes-Einstein and fractional Stokes-Einstein relations
Information on the rate of diffusion of organic molecules within secondary organic aerosol (SOA) is needed to accurately predict the effects of SOA on climate and air quality. Diffusion can be important for predicting the growth, evaporation, and reaction rates of SOA under certain atmospheric conditions. Often, researchers have predicted diffusion rates of organic molecules within SOA using measurements of viscosity and the Stokes-Einstein relation (D ∝ 1/η, where D is the diffusion coefficient and η is viscosity). However, the accuracy of this relation for predicting diffusion in SOA remains uncertain. Using rectangular area fluorescence recovery after photobleaching (rFRAP), we determined diffusion coefficients of fluorescent organic molecules over 8 orders in magnitude in proxies of SOA including citric acid, sorbitol, and a sucrose-citric acid mixture. These results were combined with literature data to evaluate the Stokes-Einstein relation for predicting the diffusion of organic molecules in SOA. Although almost all the data agree with the Stokes-Einstein relation within a factor of 10, a fractional Stokes-Einstein relation (D ∝ 1/η ) with ξ = 0:93 is a better model for predicting the diffusion of organic molecules in the SOA proxies studied. In addition, based on the output from a chemical transport model, the Stokes-Einstein relation can overpredict mixing times of organic molecules within SOA by as much as 1 order of magnitude at an altitude of ∼ 3 km compared to the fractional Stokes-Einstein relation with ξ = 0.93. These results also have implications for other areas such as in food sciences and the preservation of biomolecules.
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Predictions of diffusion rates of large organic molecules in secondary organic aerosols using the Stokes-Einstein and fractional Stokes-Einstein relations
Information on the rate of diffusion of organic molecules within secondary organic aerosol (SOA) is needed to accurately predict the effects of SOA on climate and air quality. Diffusion can be important for predicting the growth, evaporation, and reaction rates of SOA under certain atmospheric conditions. Often, researchers have predicted diffusion rates of organic molecules within SOA using measurements of viscosity and the Stokes-Einstein relation (D ∝ 1/η, where D is the diffusion coefficient and η is viscosity). However, the accuracy of this relation for predicting diffusion in SOA remains uncertain. Using rectangular area fluorescence recovery after photobleaching (rFRAP), we determined diffusion coefficients of fluorescent organic molecules over 8 orders in magnitude in proxies of SOA including citric acid, sorbitol, and a sucrose-citric acid mixture. These results were combined with literature data to evaluate the Stokes-Einstein relation for predicting the diffusion of organic molecules in SOA. Although almost all the data agree with the Stokes-Einstein relation within a factor of 10, a fractional Stokes-Einstein relation (D ∝ 1/ηξ) with ξ = 0:93 is a better model for predicting the diffusion of organic molecules in the SOA proxies studied. In addition, based on the output from a chemical transport model, the Stokes-Einstein relation can overpredict mixing times of organic molecules within SOA by as much as 1 order of magnitude at an altitude of ∼ 3 km compared to the fractional Stokes-Einstein relation with ξ = 0.93. These results also have implications for other areas such as in food sciences and the preservation of biomolecules
Interfacial photochemistry at the ocean surface is a global source of organic vapors and aerosols
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