Modeling Secondary Organic Aerosol Formation During Daytime and Nighttime via Multiphase Reactions of Phenolic Compounds

TAMANNA TASNIM, Myoseon Jang, Quang Tran Vuong, University of Florida

     Abstract Number: 43
     Working Group: Aerosol Chemistry

Abstract
Wildfires release the vast majority of gaseous pollutants that adversely impact human health and climate. When these fire plumes mix into urban atmospheres abundant in NO_x, ozone formation becomes active, impacting the air quality of the city and background air. Phenols are abundant in wildfire gases, and significantly yield Secondary Organic Aerosol (SOA) mass under the typical urban atmosphere where NO_x can gear an atmospheric oxidation cycle. Hitherto, both gas mechanisms of key phenols of fire smoke and SOA models are either nonexistent or subject to large uncertainties when simulating the impacts of wildfires and urban fires on air quality. In this study, the SOA formation is predicted using the UNIfied Partitioning Aerosol Reaction (UNIPAR) model, which can accurately predict SOA mass and aerosol properties via multiphase reactions of phenolic compounds such as phenol, cresol, and catechol, and methyl-catechol. Phenols can react with OH radicals, O₃, and NO₃ radicals. Explicitly predicted products from phenol oxidations with these three oxidation paths are employed in UNIPAR. Phenol gas mechanisms will include chemistry of RO₂ and HO₂ under different NO_x levels and the mechanisms to form Highly Oxidized Molecule including multi-hydroxybenzene and their multigeneration products. Another unique aspect of phenol chemistry is the formation of persistent phenoxy radicals, which catalytically consumes O₃ via a phenoxy-phenyl peroxide radical cycle integrated with NO_x chemistry. Gas mechanisms will be connected to the oxidation-path-dependent stoichiometric coefficients of lumping species which allow for the simulation of the atmospheric aging of phenols during daytime and nighttime ultimately allowing SOA prediction. The performance of UNIPAR is demonstrated using UF-APHOR chamber data under varying environmental conditions (daytime vs. nighttime, humidity, temperature, and NO_x levels).