With the advent of fast desktop computers, high resolution mesoscale models are being run to supply data for all types of air pollution problems. However, key algorithms in many of these transport and diffusion models have not been designed to handle the various types of data that are available from these forecast models. An example of this is found in the SLAM-P (Short-range Layered Atmospheric Model) for particulates model which handles both gaseous and particulate dispersion. Up to recently, SLAM has made use of mixing depths as a twice daily function that takes the mixing depth at a trajectory location at sunrise and sunset and uses these values to control vertical puff splitting and maximum plume growth. This procedure, while easy to implement, does have a couple of major drawbacks. First, the afternoon maximum mixing height is applied throughout daytime. However, we know that the true mixing depth increases slowly after sunrise and then grows rapidly later in the morning reaching a maximum in the afternoon. A second drawback is the assumption is that the mixing depth within a puff remains constant relative to puff movement. However, we know that the true mixing depth changes in time through advection over varying surface conditions and through different synoptic weather patterns. Through the use of hourly mixing depths from a meso-scale model (e.g., RAMS, COAMPS, etc.) it is hoped that these drawbacks will be eliminated and will produce more realistic transport layer depth calculations and splitting conditions. This paper will address the implementation of these hourly mixing depths within the SLAM modeling framework. Many examples will be presented to describe how the model is functioning under many different types weather conditions. In addition, results from selected tracer studies such as ANATEX and CAPTEX will also be presented. Finally, in conjunction with adding the hourly mixing depths in SLAM, we will explore the use of vertical eddy diffusivity profiles from a mesoscale models derived from similarity theory.