Low wind speed conditions should be studied because these conditions can present risk, particularly for areas immediately surrounding the release point, where high concentrations can occur and not dissipate. The following research attempts to clarify the processes governing both the general and low wind speed cases by determining the accuracy and uncertainty of standard prediction methods for contaminant plume transport in low wind speed plume modeling. Multiple techniques were utilized to incorporate field measured data, previously gathered for a different purpose, to generate parameter distributions and ground-truth data that could be used in stochastic models for chemical plume prediction. These data were taken during a multi-day experiment performed on Frenchman Flats, a flat, dry lakebed, at the Nevada Test Site (NTS) in February of 2007 and include weather data and chemical concentrations throughout the chemical release time. I organized these data into continuous time series for each sampling location, which were represented as vectors for the statistical and mathematical analysis. I then animated these vectors with respect to time and performed a stochastic analysis which I compared to these observed vectors. Predicted vectors of chemical concentrations, based on the statistical parameter distributions generated from the observed vectors were developed and a statistical analysis was performed on the results of the stochastic process to determine how well the model predicted the plume. It was found that stochastically modeling, with SCIPuff, of contaminant plume releases in low wind speed conditions is not accurate. This was expected because below 2 m/s, plumes no longer have a Gaussian distribution and are difficult to predict because of fluctuating winds. In fact, the model only accurately predicts the period before the plume arrives at the sensor when no plume is present. It is possible, and even probable, that stochastic modeling of contaminant plumes will provide a means to compute the bounds of a release, when coupled with a model that is accurate for low wind speed conditions and includes all the complexities of the wind field. An unexpected finding is the fact that the vertical dimension of wind movement cannot be ignored in low wind speed conditions. When planning future experiments, special attention should be paid to obtaining a good representation of the 3-D wind profile.