Predicting Downstream Concentration Histories From Upstream Data in Column Experiments

Abstract

The scales of heterogeneity present in geologic media make modeling solute transport extremely challenging, even in idealized laboratory settings. The spatial Markov model (SMM) is an anomalous transport model that has been shown to accurately capture solute transport in a broad range of highly complex and heterogeneous hydrogeologic settings. However, to date, its applications are almost entirely limited to synthetic, numerically simulated systems due to the dense data required to parameterize it, which are typically unobtainable in real experiments. Here we apply a novel SMM inverse model that required only breakthrough curve measurements from laboratory transport experiments in zeolite-packed columns that are known to display anomalous transport. We introduce an experimental design that allows for simultaneous measurements of breakthrough curves at multiple sampling locations within a one-dimensional column setup. For the first time, we apply a fully parameterized SMM to successfully predict downgradient breakthrough curves. Results show that breakthrough curve prediction accuracy significantly improves when accounting for correlation effects in these experiments, a feature that the SMM is specifically designed to capture but that most traditional anomalous transport frameworks ignore. We do so for two different Péclet numbers, providing a parsimonious framework that can potentially account for correlation statistics in different field-scale studies.