Modeling the Formation of Reactive Oxygen Species in SOA Using Explicitly Predicted Organic Products and Its Comparison with Chemical Assay Data
ANTHONY CHUNG, Myoseon Jang, University of Florida
Abstract Number: 32
Working Group: Health-Related Aerosols
Abstract
Exposure to secondary organic aerosol (SOA) can harm human health by the production of reactive oxygen species (ROS) that triggers respiratory cell death and damages lung functions. Despite the adverse impacts of SOA on public health, accurately predicting SOA composition and mechanisms of toxicity remains uncertain. This study will predict SOA toxicity by using an innovative UNIfied Particle Aerosol Reaction extended to Toxicity (UNIPAR-TOX) model. UNIPAR-TOX is a cutting-edge model that elucidates the comprehensive multiphase chemistry of precursor hydrocarbons. The model encompasses the formation of SOA through the multiphase partitioning of lumping species that originate from explicitly predicted products and processes their in-particle chemistry to form low volatile products. Lumping species in UNIPAR-TOX embodies physicochemical parameters of oxygenated products that can encode their oxidation potential productivity. Thus, the UNIPAR-TOX model enables the estimation of ROS with predicted concentrations of lumping species in SOA. This model dynamically captures SOA mass, product distributions, and ROS associated with oxidative potential with changing NOx levels and the atmospheric aging of organic products under different oxidation paths with major atmospheric oxidants (OH radical, ozone, and nitrate radicals). A toxicity database, built from chamber-generated SOA and measurements of acellular assay data (i.e., dithiothreitol (DTT) and 4-nitrophenylboronic acid (NPBA)), will facilitate the evaluation of UNIPAR-TOX’s ability to predict oxidative potential and bring quantitative insights into the mechanistic role SOAs play in adverse respiratory health. The enhanced UNIPAR-TOX algorithm will allow for the creation of a next-generation SOA model to better predict the impacts of environmental variables on air quality and cardiorespiratory health under diverse meteorological conditions.