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Chemical source parameters determination using wireless sensor networks in an arbitrary environment is a multi-faceted problem. Potential applications include security, environmental and industrial monitoring, as well as pollution control. In this paper, we propose a distributed estimation method within the Bayesian filtering framework called an iterative in-network approach, which makes use of the extended Kalman filter (EKF) and unscented Kalman filter (UKF) independently to detect and localize a chemical source and determine its emission rate. The implementation of estimation method is based on a dispersion physical model and concentration signals measured by chemical sensors in a wireless sensor networks. Simulation results show that the UKF algorithm performs better in estimation accuracy and less communication cost than the EKF.