In the event of a contaminant release, atmospheric transport and dispersion models would be used to predict the path of the contaminant plume. If monitored contaminant concentration data are available, various assimilation techniques can be applied to incorporate the data into the transport and dispersion model and thus more accurately predict the plume path. The transport and dispersion models can also be combined with other techniques to estimate unknown source characteristics or retrieve meteorological data. This work discusses how a genetic algorithm can be used to back-calculate both source and meteorological data. Both the back-calculation techniques and the assimilation methods rely on obtaining sufficient concentration data monitored by either a stationary or a mobile sensor network. To be useful, the sensor network must be sited strategically or should be evolvable to follow the plume of contaminant. Using principles from information theory, this paper discusses the requirements for developing such a network and assesses minimum data needs for defining the plume and back-calculating source characteristics and meteorological data.