11,974 research outputs found

    Kinetic modeling of Secondary Organic Aerosol formation: effects of particle- and gas-phase reactions of semivolatile products

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    The distinguishing mechanism of formation of secondary organic aerosol (SOA) is the partitioning of semivolatile hydrocarbon oxidation products between the gas and aerosol phases. While SOA formation is typically described in terms of partitioning only, the rate of formation and ultimate yield of SOA can also depend on the kinetics of both gas- and aerosol-phase processes. We present a general equilibrium/kinetic model of SOA formation that provides a framework for evaluating the extent to which the controlling mechanisms of SOA formation can be inferred from laboratory chamber data. With this model we examine the effect on SOA formation of gas-phase oxidation of first-generation products to either more or less volatile species, of particle-phase reaction (both first- and second-order kinetics), of the rate of parent hydrocarbon oxidation, and of the extent of reaction of the parent hydrocarbon. The effect of pre-existing organic aerosol mass on SOA yield, an issue of direct relevance to the translation of laboratory data to atmospheric applications, is examined. The importance of direct chemical measurements of gas- and particle-phase species is underscored in identifying SOA formation mechanisms

    Sources and Secondary Production of Organic Aerosols in the Northeastern United States during WINTER

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    Most intensive field studies investigating aerosols have been conducted in summer, and thus, wintertime aerosol sources and chemistry are comparatively poorly understood. An aerosol mass spectrometer was flown on the National Science Foundation/National Center for Atmospheric Research C‐130 during the Wintertime INvestigation of Transport, Emissions, and Reactivity (WINTER) 2015 campaign in the northeast United States. The fraction of boundary layer submicron aerosol that was organic aerosol (OA) was about a factor of 2 smaller than during a 2011 summertime study in a similar region. However, the OA measured in WINTER was almost as oxidized as OA measured in several other studies in warmer months of the year. Fifty‐eight percent of the OA was oxygenated (secondary), and 42% was primary (POA). Biomass burning OA (likely from residential heating) was ubiquitous and accounted for 33% of the OA mass. Using nonvolatile POA, one of two default secondary OA (SOA) formulations in GEOS‐Chem (v10‐01) shows very large underpredictions of SOA and O/C (5×) and overprediction of POA (2×). We strongly recommend against using that formulation in future studies. Semivolatile POA, an alternative default in GEOS‐Chem, or a simplified parameterization (SIMPLE) were closer to the observations, although still with substantial differences. A case study of urban outflow from metropolitan New York City showed a consistent amount and normalized rate of added OA mass (due to SOA formation) compared to summer studies, although proceeding more slowly due to lower OH concentrations. A box model and SIMPLE perform similarly for WINTER as for Los Angeles, with an underprediction at ages \u3c6 hr, suggesting that fast chemistry might be missing from the models

    SOA-Driven Business-Software Alignment

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    The alignment of business processes and their supporting application software is a major concern during the initial software design phases. This paper proposes a design approach addressing this problem of business-software alignment. The approach takes an initial business model as a basis in deriving refined models that target a service-oriented software implementation. The approach explicitly identifies a software modeling level at which software modules are represented as services in a technology-platformindependent way. This model-driven service-oriented approach has the following properties: (i) there is a forced alignment (consistency) between business processes and supporting applications; (ii) changes in the business environment can be traced to the application and vice versa, via model relationships; (iii) the software modules modeled as services have a high degree of autonomy; (iv) migration to new technology platforms can be supported through the platform independent software model
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