Performance of a Thermodynamic Model for Predicting Inorganic Aerosols in the Southeastern U.S.

Fine particulate matter (i.e., PM2.5) has gained intensive attention due to its adverse health and visibility degradation effects. As a significant fraction of atmospheric PM2.5, secondary inorganic PM2.5 may be formed through the gas-phase ammonia (NH3) and particle-phase ammonium (NH4+) partitioning. While partitioning of NH3-NH4+ may be simulated using a thermodynamic equilibrium model, disagreement between model predictions and measurements have been realized. In addition, the applicability of the model under different conditions has not been well studied. This research aims to investigate the applicability of a thermodynamic equilibrium model, ISORROPIA II, under different atmospheric conditions and geographic locations. Based upon the field measurements at the Southeastern Aerosol Research and Characterization (SEARCH) network, the performance of ISORROPIA II was assessed under different temperature (T), relative humidity (RH), and model setups in urban and rural locations. The impact of organic aerosol (OA) on the partitioning of NH3-NH4+ was also evaluated. Results of this research indicate that the inclusion of non-volatile cations (NVCs) in the model input is necessary to improve the model performance. Under high T (>10 °C) and low RH (<60%) conditions, ISORROPIA II tends to overpredict nitric acid (HNO3) concentration and underpredict nitrate (NO3−) concentration. The predominance of one phase of semi-volatile compound leads to low accuracy in the model prediction of the other phase. The model with stable and metastable setups may also perform differently under different T-RH conditions. Metastable model setup might perform better under high T (>10 °C) and low RH (<60%) conditions, while stable model setup might perform better under low T (<5 °C) conditions. Both model setups have consistent performance when RH is greater than 83%. Future studies using ISORROPIA II for the prediction of NH3-NH4+ partitioning should consider the inclusion of NVCs, the under/over prediction of NO3−/HNO3, the selection of stable/metastable model setups under different T-RH conditions, and spatiotemporal variations of inorganic PM2.5 chemical compositions.