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I 6. buoyancy in the unstable-to-neutrally stratified mixed layer. However,after the plume encounters the mixing lid and the stably stratified air above, its vertical motion is dampened. Dispersion modeling uses hourly averaged meteorological data, terrain elevation data, and �. emissions and source release data to compute downwind pollutant concentrations over averaging periods ranging from one hour to one year. This section presents the methodology used for the dispersion modeling analysis. The methodology is consistent with procedures documented in the EPA Guideline on Air Quality Models(Revised, 1993) and SJVUAPCD's Guide for Assessing Air Quality Impacts. Molecules of gas or small particles injected into the atmosphere will separate from each other as they are acted on by turbulent,eddies. The Gaussian mathematical model simulates the dispersion of the gas or particles within the atmosphere. The formulation of the Gaussian model is based on the following assumptions: • The predictions are not time dependent(all conditions remain unchanging with time); 'i • The wind speed and direction are uniform, both horizontally and vertically throughout the region of concern; • The rate of diffusion is not a fimction of position; and • Diffusion in the direction of the transporting wind is negligible when compared to the transport flow. The Gaussian dispersion model algorithm provides a simple analytical method of estimating v downwind concentrations,where concentration is a function of several basic elements: • Initial plume height(sum of the physical stack height and the plume rise); • The source emission rate; • The horizontal and vertical plume distribution(based on atmospheric stability); • The wind speed at source height; ` • The height of the receptor; • The off-centerline of the receptor; and • The downwind distance from the source to the receptor. 'r Screening Dispersion Modeling ` A screening dispersion modeling technique was used to initially estimate health risks due to increases in DPM impacts. The screening dispersion modeling analysis used a dispersion model to predict DPM impacts at sensitive receptors from haul trucks along roadways; the CALINE4 6. model. The CALINE4 dispersion models are conservative (tend to overpredict). The CALINE4 model is the most recent in a series of line source air quality models developed by the California Department of Transportation(Caltrans). It is based on the Gaussian diffusion equation and employs a mixing zone concept to characterize pollutant dispersion over the roadway. The purpose of the model is to assess air quality impacts near transportation facilities. Given source strength,meteorology and site geometry, CALINE4 can predict pollutant concentrations for receptors located within 500 meters of the roadway. In addition to predicting �. concentrations of relatively inert pollutants such as CO and particle concentrations; it also has B v