Engineering topics

Werkema, Dale

Publications and source records attributed to Werkema, Dale.

4 records

DOE OSTI2024

Spectral induced polarization (SIP) measurements across a PFAS-contaminated source zone

There is a pressing need for the development of field-scale, in situ screening technologies for assessing variations in aqueous film forming foam (AFFF) concentrations in soils at former fire training and storage sites. Field-scale Spectral Induced Polarization (SIP) geophysical measurements were acquired on a transect crossing an AFFF source zone. Soil samples were acquired from ten locations and used to determine variations in poly- and per-fluoroalkyl substances (PFAS) concentrations in soils and soil texture. These samples were also used to create triplicate soil columns for laboratory-grade SIP measurements. Field and laboratory observations provide evidence that SIP measurements are sensitive to the concentration of AFFF constituents associated with the pore surface in soils. The phase of the SIP measurements on the laboratory samples was linearly correlated with total soil-sorbed PFAS concentration. The phase from the field SIP measurements was highest over the location of maximum PFAS concentration measured on the laboratory samples, although a significant correlation between field-measured phase and laboratory-measured total PFAS concentration was not established. The sensitivity of the SIP response to the removal of soil PFAS using a methanol wash procedure (total PFAS concentration drop of 366 ppb) adds evidence for the case for SIP characterization of AFFF source zones. The results of these studies suggest that SIP might be developed into a field-scale technology for rapid, indirect assessment of AFFF source zones. Such a technology could improve the effectiveness of AFFF source zone characterization at reduced costs.

58 GEOSCIENCES↗

DOE OSTI2023

Geophysical signatures of soil AFFF contamination from spectral induced polarization and low field nuclear magnetic resonance methods

Few field methods are available for characterizing source zones contaminated with aqueous film forming foam (AFFF). Non-invasive geophysical characterization of AFFF source zone contamination in situ could assist with the delineation and characterization of these sites, allowing for more informed sampling regimes aimed at quantifying subsurface poly- and perfluoroalkyl substances (PFAS) contamination. In this study, we present initial results from the investigation of the sensitivity of two existing surface and borehole-deployable geophysical technologies, spectral induced polarization (SIP), and low field nuclear magnetic resonance (NMR), to soils impacted with AFFF. To investigate the sensitivity of these methods to AFFF-impacted soil, bench-scale column experiments were conducted on samples consisting of natural and synthetic soils and groundwater. While our findings do not show strong evidence of NMR sensitivity to soil PFAS contamination, we do find evidence that SIP has sufficient sensitivity to detect PFAS sorption to soils. This finding is based on evidence that AFFF constituents associated with the pore surface produce a measurable polarization response in both freshly contaminated synthetic soils and in soils historically impacted with AFFF.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Field evaluation of semi‐automated moisture estimation from geophysics using machine learning

Geophysical methods can provide three-dimensional (3D), spatially continuous estimates of soil moisture. However, point-to-point comparisons of geophysical properties to measure soil moisture data are frequently unsatisfactory, resulting in geophysics being used for qualitative purposes only. This is because (1) geophysics requires models that relate geophysical signals to soil moisture, (2) geophysical methods have potential uncertainties resulting from smoothing and artifacts introduced from processing and inversion, and (3) results from multiple geophysical methods are not easily combined within a single soil moisture estimation framework. To investigate these potential limitations, an irrigation experiment was performed wherein soil moisture was monitored through time, and several surface geophysical datasets indirectly sensitive to soil moisture were collected before and after irrigation: ground penetrating radar, electrical resistivity tomography (ERT), and frequency domain electromagnetics (FDEM). Data were exported in both raw and processed form, and then snapped to a common 3D grid to facilitate moisture prediction by standard calibration techniques, multivariate regression, and machine learning. A combination of inverted ERT data, raw FDEM, and inverted FDEM data was most informative for predicting soil moisture using a random regression forest model (one-thousand 60/40 training/test cross-validation folds produced root mean squared errors ranging from 0.025–0.046 cm 3 /cm 3 ). This cross-validated model was further supported by a separate evaluation using a test set from a physically separate portion of the study area. Machine learning was conducive to a semi-automated model-selection process that could be used for other sites and datasets to locally improve accuracy.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Application of Recursive Estimation to Heat Tracing for Groundwater/Surface‐Water Exchange

Abstract We present and demonstrate a recursive‐estimation framework to infer groundwater/surface‐water exchange based on temperature time series collected at different vertical depths below the sediment/water interface. We formulate the heat‐transport problem as a state‐space model (SSM), in which the spatial derivatives in the convection/conduction equation are approximated using finite differences. The SSM is calibrated to estimate time‐varying specific discharge using the Extended Kalman Filter (EKF) and Extended Rauch‐Tung‐Striebel Smoother (ERTSS). Whereas the EKF is suited to real‐time (“online”) applications and uses only the past and current measurements for estimation (filtering), the ERTSS is intended for near‐real time or batch‐processing (“offline”) applications and uses a window of data for batch estimation (smoothing). The two algorithms are demonstrated with synthetic and field‐experimental data and are shown to be efficient and rapid for the estimation of time‐varying flux over seasonal periods; further, the recursive approaches are effective in the presence of rapidly changing flux and (or) nonperiodic thermal boundary conditions, both of which are problematic for existing approaches to heat tracing of time‐varying groundwater/surface‐water exchange.

54 ENVIRONMENTAL SCIENCES↗