Applying the Spatial Markov Model to predicting breakthrough curves in laboratory column experiments

The Spatial Markov model (SMM) has been shown to accurately capture solute transport in highly heterogeneous porous media. However, most applications have been limited to synthetic, numerically simulated systems as the SMM typically requires dense numerical data for parameterization, typically unobtainable in real experiments. We apply a novel inverse model using only breakthrough curve measurements to data from laboratory experiments in zeolite-packed columns that 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. We apply a fully parameterized SMM to predict down-gradient breakthrough curves, with predictions matching observations favorably in a manner that other anomalous transport models cannot. We do so for two different Peclet numbers, providing a parsimonious framework that accounts for correlation statistics. .