Multi-pollutant Plume-in-grid Modeling

One-atmosphere three-dimensional (3-D) grid models are now being widely used to predict the impacts of emission controls on the atmospheric concentrations and deposition of pollutants such as ozone (O3), fine particulate matter (PM2.5), mercury (Hg) and other air toxics. Such a gridbased approach necessarily averages emissions within the volume of the grid cell where they are released. This averaging process may be appropriate for sources that are more or less uniformly distributed at the spatial resolution of the grid system. However, it may lead to significant errors for sources that have a spatial dimension much smaller than that of the grid system. For example, stack emissions lead to plumes that initially have a dimension of tens of meters, whereas the horizontal resolution in grid-based air quality models is typically several kilometers in urban applications and up to about 100 km in regional applications. This artificial dilution of stack emissions leads to (1) lower concentrations of plume material, (2) unrealistic concentrations upwind of the stack, (3) incorrect chemical reaction rates due to the misrepresentation of the plume chemical concentrations and turbulent diffusion, and (4) incorrect representation of the transport of the emitted chemicals. Plume-in-Grid (PiG) modeling has been demonstrated to be an effective approach to resolve sub-grid scale effects associated with discrete sources (e.g., Seigneur et al., 1983; Sillman et al., 1990; Kumar and Russell, 1996; Gillani and Godowitch, 1999; Karamchandani et al., 2002; 2006a; Godowitch, 2004). In this approach, the errors associated with the gridaveraging of stack emissions can be eliminated by using a subgrid-scale representation of stack plumes that is imbedded in the 3-D grid system of the air quality model. This paper describes the development and evolution of one such PiG model that was originally developed and applied for ozone (Karamchandani et al., 2002; Vijayaraghavan et al., 2006) and then extended to particulate matter (PM) (Karamchandani et al., 2006a), mercury (Karamchandani et al., 2006b; 2006c), and more recently, to investigate the sub-grid scale impacts of air toxics emissions from roadways (Karamchandani et al., 2008a). We present results from the application of the model for a number of case studies to illustrate the differences in results from traditional grid modeling versus PiG modeling, and the implication of these differences.