Predicting secondary organic aerosol formation rates and concentrations in southeast Texas

Elevated concentrations of atmospheric particulate matter are a significant public health concern, yet the sources, composition and formation mechanisms for this class of pollutants remain poorly understood. This work addresses these knowledge gaps, especially for Southeast Texas. This dissertation reports: 1) the development of a conceptual model of fine particulate matter (less than 2.5 µm in diameter, PM2.5) for Southeast Texas including estimates of primary and secondary organic aerosol (SOA) concentrations, 2) the development of a photochemical grid model tool that implements a flexible chemical mechanism and a module to predict SOA formation, and 3) the application of the tool to Southeast Texas to estimate the spatial distribution, temporal distribution, and precursors of SOA formation. PM2.5 concentrations in Southeast Texas during 2000-2001 were found to be close to, but not in excess of, the annual National Ambient Air Quality Standard for PM2.5. PM2.5 mass concentrations, composition and diurnal patterns were found to be relatively consistent throughout Southeast Texas and from season to season. The major components of PM2.5 were found to be ammonium sulfate and organic carbon; the majority of organic carbon is primary yet secondary organic carbon is significant during the ozone season. The conceptual model contributes greatly to understanding PM2.5 pollution in this area. A state-of-the-science photochemical grid model was modified to include a flexible chemical mechanism and a module to predict SOA formation. The tool was used in this work to model SOA formation rates from aromatics and monoterpenes in Southeast Texas during the ozone season, using a new chemical mechanism designed for this purpose. SOA formation was found to come predominantly from biogenic monoterpenes, particularly from α-pinene/ozone and β-pinene/nitrate-radical reactions. SOA formation rates were predicted to peak in the evening during the episode considered here. The levels, spatial distribution, and biogenic nature of the SOA formation are consistent with the limited ambient SOA information collected during this period. Sensitivity simulations showed that SOA formation is proportional to existing particulate matter concentrations and proportional to precursor emission rates. The model results are, most importantly, a guide for identifying knowledge gaps to model SOA air pollution.