In this research, the effect of dynamic data measurement on source parameters estimation is studied. The concept of mutual information is exploited to identify the optimal location for each sensor, while performing the dynamic data measurement to improve accuracy of estimation. For validation purposes, an advection - diffusion simulation code, SCIPUFF (Second-order Closure Integrated PUFF) is being used as a modeling testbed to study the effect of using dynamic data measurement. A Bayesian estimation framework is being used to characterize the source parameters, while data measurement is performed by mobile sensors, which are located based on the concept of maximizing the information content. As our numerical simulations show, using dynamic data measurement, based on maximum information collection, leads to considerably better estimates of source parameters.