Spectral induced polarization of corrosion of sulfur modified Iron in sediments
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Engineering topics
Publications and source records attributed to Freedman, Vicky L..
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An end state vision is influenced by practical constraints associated with technology limitations, resource availability, costs, timing, and unintended consequences of remedial actions, and a site’s next intended use. Therefore, subsurface environmental remediation goals often need to be balanced against the risks and costs of contaminant removal versus leaving contamination in place. For example, in regions where contaminant concentrations are high but immobile, the intended site use should be evaluated in conjunction with the risk of re-mobilization with an aggressive remedy. By contrast, the technological challenges, cost, and time to reach remedial objectives of cleaning up low levels of contamination distributed over large areas in the subsurface may require different considerations for a site’s next intended use.
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Mechanism of hexavalent chromium removal (Cr(VI) as CrO 4 2- ) by the weak-base ion exchange (IX) resin ResinTech® SIR-700-HP (SIR-700) from simulated groundwater is assessed in the presence of radioactive contaminants iodine-129 (as IO 3 - ), uranium (U as uranyl UO 2 2+ ), and technetium-99 (as TcO 4 - ), and common environmental anions sulfate (SO 4 2- ) and chloride (Cl-). Batch tests using the acid sulfate form of SIR-700 demonstrated Cr(VI) and U(VI) removal exceeded 97%, except in the presence of high SO 4 2- concentrations (536 mg/L) where Cr(VI) and U(VI) removal decreased to ≥ 80%. However, Cr(VI) removal notably improved with co-mingled U(VI) that complexes with SO 4 2- at the protonated amine sites. These U–SO 4 2- complexes are integral to U(VI) removal, as confirmed by the decrease in U(VI) removal (<40%) when the acid chloride form of SIR-700 was used instead. Solid phase characterization revealed that CrO 4 2- is removed by IX with SO 4 2- complexes and/or reduced to amorphous Cr(III)(OH) 3 at secondary alcohol sites. Tc(VII)O 4 - and I(V)O 3 - also undergo chemical reduction, following a similar removal mechanism. Oxyanion removal preference is determined by the anion reduction potential (CrO 4 2 - >TcO 4 - >IO 3 - ), geometry, and charge density. For these reasons, 39% and 69% of TcO 4 - and 17% and 39% of IO 3 - are removed in the presence and absence of Cr(VI), respectively.
Ion exchange (IX) resins are used in pump-and-treat (P&T) facilities to remove soluble groundwater contaminants. However, natural anions present at concentrations orders of magnitude higher than contaminants can compete for IX sites and impact resin lifecycles. Here, the Hanford Site’s 200 West Area P&T facility (Washington State, USA) was selected as a case study because it currently uses two IX resins: Purolite® A532E (A532E) to remove pertechnetate (TcO 4 - ) and DOWEX 21K (DOWEX) to remove uranium from groundwater. Nitrate (NO 3 - ), sulfate (SO 4 2- ), chloride (Cl - ), and carbonate (CO 3 2- ) anions have been identified to potentially compete for A532E and DOWEX IX sites. Hanford-relevant anion groundwater concentrations were used to design a series of laboratory-scale batch experiments to evaluate the impact of competing anions on resin performance and potential kinetic effects. These data are then modeled to obtain Cl--normalized equilibrium exchange coefficients (K) needed to predict IX resin performance. The work is presented in two parts, with IX performance evaluated for A532E in Part I and DOWEX in Part II. Part I results demonstrate that TcO 4 - uptake is not impacted by NO 3 - , SO 4 2- , Cl - , CO 3 2- (as HCO 3 - ) and U(VI) carbonate anions, with K TcO4-/Cl- > 4,000, likely due to the high selectivity of A532E trihexylammonium sites for the large, weakly hydrated TcO 4 - anion. Other anion K values were K NO3-/Cl- = 20, K SO4--/Cl- = 0.2, K HCO3-/Cl- = 0.09, K U/Cl- = 370–1000. These K values provide conservative parameters for predicting A532E performance, and demonstrate that, under these test conditions, A532E will remove TcO 4 - from current and future influent streams to meet groundwater treatment objectives.
The selectivity of ion exchange (IX) resins for aqueous contaminant removal can be impacted by changing concentrations of competing natural groundwater ions. In a two-part investigation, the Hanford Site 200 West Area pump-and-treat (P&T) facility in Washington State, USA is used as a case study to evaluate the performance of two IX resins for groundwater treatment: Purolite® A532E for pertechnetate (TcO 4 - ) removal, explored in Part I, and DOWEX 21K (DOWEX) for uranium (U) removal. In Part II, DOWEX selectivity for U, as uranyl carbonate species, and uptake kinetics is quantified in a series of laboratory-scale aqueous batch experiments containing Hanford-relevant concentrations of competing anions nitrate (NO 3 - ), sulfate (SO 4 2- ), chloride (Cl - ), and carbonate (CO 3 2- ), as well as co-mingled contaminant TcO 4 - . Here the results demonstrate that DOWEX trimethylammonium functional groups are highly selective for U carbonate species (85–100 % uptake) under all conditions investigated. Only NO 3 - concentrations of 100 mM were shown to decrease U removal, with the extent (85–99 %) depending on competing anion concentrations present in solution. However, at the highest NO 3 - concentrations reported for groundwaters treated at the P&T facility (25 mM), the effect on U uptake is minimal. The batch sorption results are modeled to obtain chloride normalized equilibrium exchange coefficients (K) for predicting DOWEX performance: K SO4--/Cl- = 2.0, K NO3-/Cl- = 5.0, K HCO3-/Cl- = 1.5, K TcO4-/Cl- = 2,000, and K U/Cl- = 50,000. These K values predict little effect of current and future influent chemistries on U removal by DOWEX, where both uranyl carbonate species and TcO 4 - are removed such that effluent concentrations meet groundwater treatment requirements.
Here in this study, we developed a deep learning (DL) framework with a multi-channel three-dimensional convolutional neural network (MC3D-CNN) to predict well performance and thereby assist future environmental remediation design. Such prediction of extraction well performance at designated locations is critical for configuring pump-and-treat (P&T) well network design and operation, setting reasonable target closure dates for overall remedying, and estimating remedy costs. The framework is developed with operational and monitoring data routinely collected during P&T remedy operations, including well extraction and injection rates as well as in situ contaminant concentrations. Traditionally, the collected data were rarely used for purposes other than assessing past well performance and the accuracy of the conceptual site model. However, recent advances in data-driven computational approaches enable better use of the large datasets to inform future well performance, enhance site characterization, and improve remediation planning. In this study, we established a DL framework to integrate transient three-dimensional contaminant plumes and multiple aquifer properties (e.g., hydraulic conductivity and hydrostratigraphic maps) to identify characteristic patterns controlling and representing extraction well mass recovery, aiming at providing future mass recovery estimates for existing wells and candidate wells at any proposed locations. We evaluated our framework by using a realistic synthetic dataset generated from a well-calibrated flow and transport model used in the 200 West Area of the U.S. Department of Energy’s Hanford Site in southeastern Washington state. The multi-channel feature in our framework allows integration of various types and temporal densities of training datasets for DL model development. Overall, we found that the trained DL model achieved an accuracy of over 90% in ranking extraction well performance in validation datasets, and over 80% in predicting high-performance-ranking well locations. This data-informed approach provides a flexible tool to support adaptive site management, streamline decision-making, and potentially reduce remediation time and costs. Our DL framework can be used as a filtering tool to improve the current P&T network optimization design by reducing the number of candidate well locations.