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Process-Level Modeling Can Simultaneously Explain Secondary Organic Aerosol Evolution in Chambers and Flow Reactors.
He, Yicong; Lambe, Andrew T; Seinfeld, John H; Cappa, Christopher D; Pierce, Jeffrey R; Jathar, Shantanu H.
Afiliación
  • He Y; Department of Mechanical Engineering, Colorado State University, Fort Collins, Colorado 80523, United States.
  • Lambe AT; Aerodyne Research Inc., Billerica, Massachusetts 01821, United States.
  • Seinfeld JH; Divison of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, California 91125, United States.
  • Cappa CD; Department of Civil and Environmental Engineering, University of California Davis, Davis, California 95616, United States.
  • Pierce JR; Department of Atmospheric Science, Colorado State University, Fort Collins, Colorado 80521, United States.
  • Jathar SH; Department of Mechanical Engineering, Colorado State University, Fort Collins, Colorado 80523, United States.
Environ Sci Technol ; 56(10): 6262-6273, 2022 05 17.
Article en En | MEDLINE | ID: mdl-35504037
ABSTRACT
Secondary organic aerosol (SOA) data gathered in environmental chambers (ECs) have been used extensively to develop parameters to represent SOA formation and evolution. The EC-based parameters are usually constrained to less than one day of photochemical aging but extrapolated to predict SOA aging over much longer timescales in atmospheric models. Recently, SOA has been increasingly studied in oxidation flow reactors (OFRs) over aging timescales of one to multiple days. However, these OFR data have been rarely used to validate or update the EC-based parameters. The simultaneous use of EC and OFR data is challenging because the processes relevant to SOA formation and evolution proceed over very different timescales, and both reactor types exhibit distinct experimental artifacts. In this work, we show that a kinetic SOA chemistry and microphysics model that accounts for various processes, including wall losses, aerosol phase state, heterogeneous oxidation, oligomerization, and new particle formation, can simultaneously explain SOA evolution in EC and OFR experiments, using a single consistent set of SOA parameters. With α-pinene as an example, we first developed parameters by fitting the model output to the measured SOA mass concentration and oxygen-to-carbon (OC) ratio from an EC experiment (<1 day of aging). We then used these parameters to simulate SOA formation in OFR experiments and found that the model overestimated SOA formation (by a factor of 3-16) over photochemical ages ranging from 0.4 to 13 days, when excluding the abovementioned processes. By comprehensively accounting for these processes, the model was able to explain the observed evolution in SOA mass, composition (i.e., OC), and size distribution in the OFR experiments. This work suggests that EC and OFR SOA data can be modeled consistently, and a synergistic use of EC and OFR data can aid in developing more refined SOA parameters for use in atmospheric models.
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Contaminantes Atmosféricos Tipo de estudio: Prognostic_studies Idioma: En Revista: Environ Sci Technol Año: 2022 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Contaminantes Atmosféricos Tipo de estudio: Prognostic_studies Idioma: En Revista: Environ Sci Technol Año: 2022 Tipo del documento: Article País de afiliación: Estados Unidos