Based on Bayes theorem and combined variance assumptions on pollutant concentration time series with Adaptive-Metropolis sampling, a modular Bayesian approach was established targeting at pollutant source identification during spills. This probability approach updated the prior knowledge on source information by combining experiments and monitoring and was able to directly characterize uncertainty due to the inversion process by probability distribution. Source inversion test results from field tracer experiments were investigated to determine the validity of this Bayesian inference approach, correlation of posterior parameter and impact factors. Results indicate that Bayesian approach was successful in identifying the source parameters and could effectively reduce the emergency decision risks. It is shown that the skewness of posterior distribution of source parameters and variation were sensitive to assumed variance. Using RMSE as objective function, test results also suggested that the default parameters for the established Bayesian source inversion method, were as follows:heteroscedasticity setting stabilization factors λ₁=0, and λ₂=0.1-0.5, and AM sampling proposal scale factor s_d=0.1-0.3. Comparisons between the Bayesian approach and optimization approach on aspects of solution methodology, computing process and inverse results were made and differentiation were highlighted. This work provides valuable references for the practical usage of Bayesian approach in surface water pollution source identification.