A Bayesian-based integrated approach for identifying groundwater contamination sources

Abstract

The identification of groundwater contamination sources (GCS) can provide comprehensive knowledge for the remediation and risk assessment of contaminated sites. This study develops an innovative framework that can be effectively and efficiently used for the optimal sampling well location design and GCS parameters identification. The framework is based on Bayesian theory and integrates the relative entropy (RE), a 0-1 integer programming optimization model (0-1 IPOM), Markov Chain Monte Carlo (MCMC), and a Kriging surrogate model (KSM). The expected RE is used to quantify information about unknown parameters from concentration measurements based on a Bayesian design. The optimal sampling well locations are determined through the comprehensive application of 0-1 IPOM and the expected RE. After determining the optimal sampling well locations, a Bayesian approach based on MCMC is employed to identify the GCS parameters. However, such problems are time-consuming because both determination and identification require the contaminant transport model to be run multiple times. To address this challenge, a KSM is constructed for the contaminant transport model, which greatly accelerates the determination and identification processes. The feasibility and accuracy of the proposed approach are verified by two hypothetical numerical case studies. The results show that the developed Bayesian-based integrated approach can be accurately and effectively applied for optimal sampling well location design and GCS parameter identification. Overall, this study highlights that the Bayesian-based integrated approach represents a promising solution for GCS parameter identification.