Most of the models for near-road air quality study are based on either the steady-state Gaussian equations or Lagrangian puff formulations to describe the non-reactive pollutant concentrations at receptor sites. The accuracy of the dispersion calculation is often limited due to simplifications that are necessary to derive the analytical solutions for the dispersion equations. In addition, the general source-receptor structure of these models limits their capabilities to treat rapid chemical and physical processes that were recently observed in the near-road environment. A new three-dimensional Eulerian near-road air quality model, the Texas A&M Near-Road Model (TAMNROM-3D), was developed as part of a traffic congestion mitigation study that aims to use air pollutant concentrations as constrains in traffic planning. The enhanced pollutant turbulent diffusion due to moving vehicles is handled in TAMNROM-3D by a recently developed vehicle induced turbulence parameterization scheme. In this paper, the performance of TAMNROM-3D on non-reactive tracer dispersion is evaluated using the SF6 dataset collected by the General Motors (GM) during the 1975 Sulfate Dispersion Experiment. The predicted concentrations show excellent agreement with observation under different wind conditions. Model performance statistics are compared favorably with those reported by the ROADWAY-2 model. Sensitivitystudies indicate that the model is robust to reasonable uncertainties in the model parameters and input data. The 3D Eulerian framework allows an intuitive representation of surface and elevated highways and complex highway intersections, and easy integration of chemical/physical processes in the model to simulate the detailed transformation of air pollutants in a near-road environment.
Abstract