Oak Ridge National Laboratory Site Research Articles

Stochastic estimation of aquifer geometry using seismic refraction data with borehole depth constraints

We develop a Bayesian model to invert surface seismic refraction data with depth constraints from boreholes for characterization of aquifer geometry and apply it to seismic and borehole data sets collected at the contaminated Oak Ridge National Laboratory site in Tennessee. Rather than the traditional approach of first inverting the seismic arrival times for seismic velocity and then using that information to aid in the spatial interpolation of wellbore data, we jointly invert seismic first arrival time data and wellbore‐based information, such as depths of key lithological boundaries. We use a staggered‐grid finite difference algorithm with second‐order accuracy in time and fourth‐order accuracy in space to model seismic full waveforms and use an automated method to pick the first arrival times. We use Markov Chain Monte Carlo methods to draw many samples from the joint posterior probability distribution, on which we can estimate the key interfaces and their associated uncertainty as a function of horizontal location and depth. We test the developed method on both synthetic and field case studies. The synthetic studies show that the developed method is effective at rigorous incorporation of multiscale data and the Bayesian inversion reduces uncertainty in estimates of aquifer zonation. Applications of the approach to field data, including two surface seismic profiles located 620 m apart from each other, reveal the presence of a low‐velocity subsurface zone that is laterally persistent. This geophysically defined feature is aligned with the plume axis, suggesting it may serve as an important regional preferential flow pathway.

Temporal Changes in Soil Partitioning and Bioaccessibility of Arsenic, Chromium, and Lead

The hazard imposed by trace element contaminants within soils is dependent on their ability to migrate into water systems and their availability for biological uptake. The degree to which a contaminant may dissociate from soil solids and become available to a target organism (i.e., bioaccessibility) is therefore a determining risk factor. We used a physiologically based extraction test (PBET) to estimate the bioaccessible fraction of arsenic-, chromium-, and lead-amended soil. We investigated soils from the A and B horizons of the Melton Valley series, obtained from Oak Ridge National Laboratory site, to address temporal changes in bioaccessibility. Additionally, common extractions that seek to define reactive pools of metals were employed and their correlation to PBET levels evaluated. With the exception of Pb amended to the A horizon, all other treatments exhibited an exponential decrease in bioaccessibility with incubation time. The bioaccessible fraction was less than 0.2 mg kg(-1) within 30 d of incubation for As and Cr in the A horizon and for As and Pb within the B horizon; Cr in the B horizon declined to nearly 0.3 mg kg(-1) within 100 d of aging. The exchangeable fraction declined with incubation period and, with the exception of Pb, was highly correlated with the decline in bioaccessibility. Our results demonstrate limited bioaccessibility in all but one case and the need to address both short-term temporal changes and, most importantly, the soil physiochemical properties. They further reveal the importance of incubation time on the reactivity of such trace elements.

Prioritizing R&D for the U.S. Department of Energy's Weapons Complex Clean-Up

Prioritizing the U.S. Department of Energy's (DOE's) targeted R&D program supporting weapons-complex clean-up has been problematic. Considering issues common to all R&D evaluation, linking R&D prioritization to clean-up project prioritization, the latter aligned with institutional goals, offers a pragmatic solution. This method is illustrated with results of a clean-up prioritization model of DOE's Oak Ridge National Laboratory site.