Assessing human health risk of groundwater DNAPL contamination by quantifying the model structure uncertainty

Abstract

The contamination of groundwater with dense non-aqueous phase liquids (DNAPL) can pose long-term risks to human health. Due to the complexity of geological environments and the properties of DNAPL, a key step in human health risk (HHR) assessment is the establishment of a reliable DNAPL transport model. In this study, we evaluate the impact of the model structure uncertainty on DNAPL transport modeling for HHR assessment. First, Gaussian process regression (GPR) was used to learn the structure error of the DNAPL transport model through statistical simulations, and then, Markov chain Monte Carlo (MCMC) simulation was used to calibrate the parameters of the physical model and GPR. On the basis of two case studies, namely, a sandbox experiment and a synthetic DNAPL transport model investigation, the results demonstrated that ignoring the model structure error will lead to deviations during parameter calibrations and model predictions. This is because the model parameters could be overfitted to compensate for the model structure error during the calibration process. Results of variance decomposition verified that the model structure error was the main uncertainty source in DNAPL transport modeling for both of the case studies. The DNAPL transport modeling and HHR assessment conducted by quantifying the uncertainties of the model parameters and structure simultaneously was found to be more reliable than that by calibrating only the model parameters. In addition, the HHR assessment of groundwater DNAPL contamination without considering the model structure error could lead to misleading depictions of the key risk zone. We propose that GPR is an effective technique to quantitatively describe the structure error of DNAPL transport models.