Online parameter identification of advection-diffusion processes is performed using a mobile sensor network. A constrained cooperative Kalman filter is developed to provide estimates of the field values and gradients along the trajectories of the mobile sensor network so that the temporal variations of the field values can be estimated. Utilizing the state estimates from the constrained cooperative Kalman filter, a recursive least square (RLS) algorithm is designed to estimate the unknown parameters of the advection-diffusion process. We provide bias analysis of the RLS in the paper. In addition to validating the proposed algorithm in simulated advection-diffusion fields, we build a controllable CO2 advection-diffusion field in a lab and design a sensor grid that collects the field concentration over time to allow the validation of the proposed algorithm in the CO2 field. Experimental results demonstrate robustness of the algorithm under realistic uncertainties and disturbances.