## Applicability of Bayesian inference approach for pollution source identification of river chemical spills: A tracer experiment based analysis of algorithmic parameters, impacts and comparison with Frequentist approaches

(2.School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, China 518055)

**【Abstract】**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 indicated 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 λ_{1 }= 0, and λ_{2 }= 0.1–0.5, and AM sampling proposal scale factor 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.

**【Keywords】 **Bayesian inference; source inversion; river chemical spill; Adaptive-Metropolis sampling; river tracer experiments;

**【DOI】**

**【Funds】**

**References**

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ISSN:1000-6923

CN:11-2201/X

Vol 37, No. 10, Pages 3813-3825

October 2017

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