A visualization tool for EPA’s Community Multiscale Air Quality modeling system, VisCMAQ, which is developed in this study as an OpenGL based visualization tool that has the capability to render data on both regular and irregular grids. The goal of this project is to enhance the VisCTM model, which was created last summer with additional flexibilities such as reading from NetCDF files. Also, the ability to process multiple time steps for multiple species at the same time. VisCMAQ would be able to read, and visualize different types of data that is written in C Language. These enhancements will allow the new VisCMAQ to accept an incorporation of data sets for various spatial domains and sets of chemical species. At this process the Visualization of the output from Multi scale Air Quality (VisCMAQ) model will be created. This VisMAQ project will link VisCTM tools with the Environmental Protection Agency’s Community Multi-scale Air Quality (CMAQ) Model system; which will utilize VisCTM import files written by the IOAPI library that is built upon the NetCDF file format. Key terms: Visualization, Irregular grid, volume rendering, OPEN-GL, Vis5D, VisCTM, CMAQ. ________________________________________________________________________ ____ Presenter/Authors Biography Dr. Thomas Jyh-Cheng Liu is an Associate Professor in Computer Science of NJCU (New Jersey City University). He is a Director and Principal Investigator of NSF grant PEARL (Performance Education and Research Laboratory) at NJCU. He holds a Ph.D. from University of Illinois at Chicago. He was a senior software Engineer in Bell Labs of Lucent Technology. He is a senior member of IEEE and an active member of ACM. He is also a member of National Honor Societies Tau Beta Pi and Phi Kappa Phi. Solomon O. Mainye and Michael V. Khalil were both Senior undergraduate students of computer science in New Jersey City University. Dr. Robert Bennett and Dr. Douglas Wright are scientists at Brookhaven National Laboratory.

A six‐moment microphysics module for sulfate aerosols based on the quadrature method of moments has been incorporated in a host 3‐D regional model, the Multiscale Air Quality Simulation Platform. Model performance was examined and evaluated by comparison with in situ observations over the eastern United States for a 40‐day period from 19 July to 28 August 1995. The model generally reproduces the spatial patterns (sulfate mixing ratios and wet deposition) over the eastern United States and time series variations of sulfate mass concentrations. The model successfully captured the observed size distribution in the accumulation mode (radius 0.1–0.5 μm), in which the sulfate is predominately located, while underestimating the nucleation and coarse modes on the basis of the size distributions retrieved from the modeled six moments at the Great Smoky Mountains (GSM). This is consistent with better model performance on the effective radius (ratio of third to second moment, important for light scattering) than on number‐mean and mass‐mean radii. However, the model did not predict some of the moments well, especially the higher moments and during the dust events. Aerosol components other than sulfate such as dust and organics appear to have contributed substantially to the observed aerosol loading at GSM. The model underpredicted sulfate mixing ratios by 13% with about 50% of observations simulated to within a factor of 2. One of the reasons for this underestimation may be overprediction of sulfate wet deposition. Sulfate mass concentrations and number concentrations were high in the source‐rich Ohio River valley, but number concentrations were also high over the mid‐Atlantic coast (New Jersey area). Most (77%) sulfate amount was below 2.6 km, whereas most sulfate number (>52%) was above 2.6 km except over Ohio River valley (41%). These results demonstrate the accuracy, utility, practicality, and efficiency of moment‐based methods for representing aerosol microphysical processes in large‐scale chemical transport models.

Important aerosol properties and processes depend on their size distribution: light scattering, cloud nucleating properties, dry deposition, and penetration into airways of lungs. The evolution of the mass loading itself depends on particle size because of the size dependence of growth and removal processes. For these reasons it is increasingly recognized that chemical transport and transformation models must represent not just the mass loading of atmospheric particulate matter but also the aerosol microphysical properties and the evolution of these properties if aerosols are to be accurately represented in these models. If the size distribution of the aerosol is known, a given property can be evaluated as the integral of the appropriate kernel function over the size distribution. This has motivated the approach of determining aerosol size distribution, and of explicitly representing this distribution and its evolution in chemical transport models. Atmospheric chemical transport models to date mainly represent only mass of aerosol constituents. Part of the reason for this is inadequate understanding of the key processes that govern the aerosol size distribution: