This paper presents a sequential optimum design approach for estimating the parameters of an atmospheric dispersion process model based on measurement data gathered by a team of cooperating sensor-equipped UAVs. Locally optimal waypoint sequences that account for each UAV's possibly heterogeneous motion dynamics are computed by minimizing a suitable optimality criterion. Following these waypoints, the UAVs cooperatively maximize the information gain of the acquired measurements. A decentralized data-driven online control scheme is proposed that couples parameter estimation, waypoint calculation, and vehicle control and enables the UAVs to adaptively observe the dynamic process and iteratively improve the parameter estimate. Simulations demonstrate the effectiveness of the proposed scheme in reducing the error between the estimated and the true dispersion model parameters compared to non-adaptive sensing strategies. In addition, the effect of using different optimality criteria, different numbers and types of UAVs as well as two options for decentralizing the waypoint calculation are investigated.