Predicting Benzene Transport in Subsurface under Uncertainty through a Coupled Monte Carlo and Factorial Analysis Approach

Abstract

Mathematical models have been widely used to simulate fate and transport of the nonaqueous phase liquids (NAPLs) contaminants for risk assessment and remediation design studies. However, many theoretical and field studies have recognized that the contaminant fate in subsurface is significantly influenced by uncertainties inherent in natural porous media and thus may affect model predictions. To tackle such a difficulty, a coupled Monte Carlo and factorial analysis modeling approach was developed in this study to systematically investigate impacts of uncertainties associated with hydrocarbon-contaminant transport in subsurface. The approach integrated a solute transport model, factorial analysis, and Monte Carlo technique into a general framework and effectively analyzed the individual and joint effects of input parameters' uncertainties that are associated with hydrogeological conditions. Through a hypothetical case study, the results demonstrated that the uncertainties in input parameters pose considerable influences on the predicted output. The results obtained from the systematic uncertainty analysis methods proposed in this study, such as mean, standard deviation, and percentile, can provide useful information for further decision making regarding the petroleum contamination problem.