PNNL

Abstract

High concentrations of PM2.5 (particulate matter with an aerodynamic diameter less than 2.5?µm) in China have caused severe visibility degradation. Accurate simulations of PM2.5 and its chemical components are essential for evaluating the effectiveness of pollution control strategies and the health and climate impacts of air pollution. In this study, we compared the GEOS-Chem model simulations with comprehensive data sets for organic aerosol (OA), sulfate, nitrate, and ammonium in China. Model results are evaluated spatially and temporally against observations. The new OA scheme with a simplified secondary organic aerosol (SOA) parameterization significantly improves the OA simulations in polluted urban areas. The model underestimates sulfate and overestimates nitrate for most of the sites throughout the year. More significant underestimation of sulfate occurs in winter, while the overestimation of nitrate is extremely large in summer. Our model is unable to capture some of the main features in the diurnal pattern of the PM2.5 chemical components, suggesting underrepresented processes. Potential model adjustments that may lead to a better representation of boundary layer height, precursor emissions, hydroxyl radical, heterogeneous formation of sulfate and nitrate, and the wet deposition of nitric acid and nitrate are tested in the sensitivity analysis. The results suggest that uncertainties in chemistry perhaps dominate the model bias. The proper implementation of heterogeneous sulfate formation and the good estimates of the concentrations of sulfur dioxide and hydroxyl radical are essential for the improvement of the sulfate simulation. The update of the heterogeneous uptake coefficient of nitrogen dioxide significantly reduces the modeled concentrations of nitrate, and accurate sulfate simulation is important for modeling nitrate. However, the large overestimation of nitrate concentrations remains in summer for all tested cases. The uncertainty of the production of nitrate cannot explain the model overestimation, suggesting a problem related to the removal. A better understanding of the atmospheric nitrogen budget is needed for future model studies. Moreover, the results suggest that the remaining underestimation of OA in the model is associated with the underrepresented production of SOA.