Regional Modeling of SOA Formation Considering Aqueous Chemistry under Different Humidities

YUJIN JO, Myoseon Jang, University of Florida

     Abstract Number: 276
     Working Group: Aerosol Chemistry

Abstract
Secondary Organic Aerosol (SOA) contributes a significant fraction of particulate matter, and influences regional air quality and climate. SOA forms through complicated atmospheric oxidation paths of various Volatile Organic Compounds (VOCs). The spatial extent of SOA in the United State is different in the dry and humid regions, and also exhibits strong seasonality, connoting the contributors of SOA formation vary regionally and temporally. Since SOA formation is complex due to complexity in organic species and reaction mechanisms, it is challenging to accurately represent SOA in regional models. The prediction of SOA mass concentrations in regional scales is traditionally performed by using gas-particle partitioning models of few surrogate products. SOA growth via acid catalyzed heterogeneous reaction of isoprene products has recently been added to regional models. However, current air quality models have no feature to process aqueous reactions of a variety of oxidized organic products from different hydrocarbons. This limitation leads historically underestimated SOA mass concentration in the regional-scale air quality models as compared with observation, particularly in wet regions or humid seasons. In this study, the prediction of regional SOA mass concentrations is performed over the United State domain by using the Comprehensive Air quality Model with extensions (CAMx) coupled with newly developed UNIfied Partitioning-Aerosol phase Reaction (UNIPAR), which simulates the SOA formation via multiphase reactions of hydrocarbons. The CAMx-UNIPAR model includes multiphase partitioning and emerging chemistry (i.e., lowly volatile organic products via gas phase autoxidation, oligomerization of organic products in organic phase, aqueous reactions, and organosulfate formation). The SOA simulation of CAMx-UNIPAR is also compared to that predicted with the pre-existing SOAP model. The prediction of SOA mass concentrations with CAMx-UNIPAR is compared to observed field data. The CAMx-UNIPAR can improve the prediction of organic carbon concentrations in the presence of electrolytic inorganic aerosol for the humid regions.