How long do natural waters “remember” release incidents of Marcellus Shale waters: a first order approximation using reactive transport modeling

Natural gas production from the Marcellus Shale formation has significantly changed energy landscape in recent years. Accidental release, including spills, leakage, and seepage of the Marcellus Shale flow back and produced waters can impose risks on natural water resources. With many competing processes during the reactive transport of chemical species, it is not clear what processes are dominant and govern the impacts of accidental release of Marcellus Shale waters (MSW) into natural waters. Here we carry out numerical experiments to explore this largely unexploited aspect using cations from MSW as tracers with a focus on abiotic interactions between cations released from MSW and natural water systems. Reactive transport models were set up using characteristics of natural water systems (aquifers and rivers) in Bradford County, Pennsylvania. Results show that in clay-rich sandstone aquifers, ion exchange plays a key role in determining the maximum concentration and the time scale of released cations in receiving natural waters. In contrast, mineral dissolution and precipitation play a relatively minor role. The relative time scales of recovery τrr, a dimensionless number defined as the ratio of the time needed to return to background concentrations over the residence time of natural waters, vary between 5 and 10 for Na, Ca, and Mg, and between 10 and 20 for Sr and Ba. In rivers and sand and gravel aquifers with negligible clay, τrr values are close to 1 because cations are flushed out at approximately one residence time. These values can be used as first order estimates of time scales of released MSW in natural water systems. This work emphasizes the importance of clay content and suggests that it is more likely to detect contamination in clay-rich geological formations. This work highlights the use of reactive transport modeling in understanding natural attenuation, guiding monitoring, and predicting impacts of contamination for risk assessment.