Current status of air quality models for particulate matter

The current status of the mathematical modeling of atmospheric particulate matter (PM) is reviewed in this paper. Simulating PM requires treating vari ous processes, including the formation of condensable species, the gas/part icle partitioning of condensable compounds, and in some cases, the evolutio n of the particle size distribution. The algorithms available to simulate t hese processes are reviewed and discussed. Eleven 3-dimensional (3-D) Euler ian air quality models for PM are reviewed in terms of their formulation an d past applications. Results of past performance evaluations of 3-D Euleria n PM models are presented. Currently, 24-hr average PM2.5 concentrations ap pear to be predicted within 50% for urban-scale domains. However, there are compensating errors among individual particulate species. The lowest error s tend to be associated with SO42-, while NO3-, black carbon (BC), and orga nic carbon (OC) typically show larger errors due to uncertainties in emissi ons inventories and the prediction of the secondary OC fraction. Further im provements and performance evaluations are recommended.