Model Complexity Needed for Quantitative Analysis of High Resolution Isotope and Concentration Data from a Toluene-Pulse Experiment

Separating microbial- and physical-induced effects on the isotope signals of contaminants has been identified as a challenge in interpreting compound-specific isotope data. In contrast to simple analytical tools, such as the Rayleigh equation, reactive-transport models can account for complex interactions of different fractionating processes. The question arises how complex such models must be to reproduce the data while the model parameters remain identifiable. In this study, we reanalyze the high-resolution data set of toluene concentration and toluene-specific delta C-13 from the toluene-pulse experiment performed by Qiu et al. (this issue). We apply five reactive-transport models, differing in their degree of complexity. We uniquely quantify degradation and sorption properties of the system for each model, estimate the contributions of biodegradation-induced, sorption-induced, and transverse-dispersion-induced isotope fractionation to the overall isotope signal, and investigate the error introduced in the interpretation of the data when individual processes are neglected. Our results show that highly resolved data of both concentration and isotope ratios are needed for unique process identification facilitating reliable model calibration. Combined analysis Of these highly resolved data demands reactive transport models accounting for nonlinear degradation kinetics and isotope fractionation by both reactive and physical processes such as sorption and transverse dispersion.