Groundwater Remediation Process Research Articles

Laboratory Flushing Tests of Dissolved Contaminants in Heterogeneous Porous Media with Low-Conductivity Zones

The retention of contaminants within low-conductivity regions such as clay lenses and aquitards can greatly affect groundwater remediation processes. The aim of this study was to experimentally investigate the effects of the geometry of low-conductivity zones, conductivity contrast, and flow regime on solute flushing. We conducted a series of flushing tests in cylindrical models containing a cylindrical low-conductivity zone (i.e., low-K zone) embedded in a highly conductive medium (i.e., high-K zone). Seven models comprising four high-conductivity-contrast (SL, SS, LL, and LS), one medium-contrast (LLM), one low-contrast (LLL), and one homogeneous (H) models were considered. Experiments were conducted at two flow rates (Q = 0.6 and 26 cm3/min) for each heterogeneous model (SL, SS, LL, LS, LLM, and LLL) to compare the flushing processes in different flow regimes. First, we verified the validity of our experiments by comparing the results of the H model from an analytical solution with our experiment. The results of the high-contrast models showed that for a diffusion-dominated regime (Q = 0.6 cm3/min), the pore volume injected (PVI) required to flush out solute mass was much smaller than that in an advection-dominated regime (Q = 26 cm3/min). To evaluate the pore volumes required to flush out solutes for the four high-contrast models, we introduced a parameter P0.01, which is defined as the PVI needed for the relative concentration to become 0.01 at the middle of the low-K zone. P0.01 decreases with increasing the specific surface area of the low-K zone for diffusion-dominated regimes, while it increases with increasing the length of the low-K zone for advection-dominated regimes. We also determined the importance of the effect of K contrast on solute retention by comparing the results of three different models of K contrast (LL, LLM, and LLL).

Evaluation of Zero-Valent Iron-Based Reductants for Groundwater Remediation Process: Self-Oxidation, Selectivity, and Mechanism

Evaluation of Zero-Valent Iron-Based Reductants for Groundwater Remediation Process: Self-Oxidation, Selectivity, and Mechanism

Influence of several crucial groundwater components on the toxicity of nanoscale zero-valent iron towards Escherichia coli under aerobic and anaerobic conditions

In this paper, the effects of several groundwater components (heavy metals, inorganic anions, and organics) on the cytotoxicity of nanoscale zero-valent iron (NZVI) towards Escherichia coli (E. coli) under aerobic/anaerobic conditions were studied. The results showed that NZVI exhibited much higher toxicity in anaerobic conditions than aerobic conditions. Under the state of air-saturation, corrosion of NZVI occurred rapidly, at the same time, it could stably and continuously generate Fe (Ⅱ) and trigger reactive oxygen species (ROS), which led to oxidative stress in E. coli. While in the deareated state, the TEM images showed that the integrity of the cell membrane was destroyed, which validated that the main mechanism of NZVI cytotoxicity was the rapid membrane damage of E. coli. The presence of Cr (Ⅵ) reduced the toxicity of NZVI through oxidation-reduction with NZVI, especially under anaerobic conditions. In contrast, the presence of Cd (Ⅱ) could be adsorbed onto NZVI to increase the cytotoxicity of NZVI. The presence of phosphate and humic acid greatly improved the survival rate of E. coli through the complex reaction with Fe (Ⅱ), especially under aerobic conditions. On the one hand, the formed Fe (II)-phosphate/humic acid complex could reduce the production of ROS. On the other hand, the complex accumulated on the outer surface of E. coli cells could provide steric hindrance to impede the contact between NZVI and cell. These findings were crucial for practical significance to evaluate environmental risk during the groundwater remediation process by using NZVI.

Optimization of the Groundwater Remediation Process Using a Coupled Genetic Algorithm-Finite Difference Method

In situ chemical oxidation using permanganate as an oxidant is a remediation technique often used to treat contaminated groundwater. In this paper, groundwater flow with a full hydraulic conductivity tensor and remediation process through in situ chemical oxidation are simulated. The numerical approach was verified with a physical sandbox experiment and analytical solution for 2D advection-diffusion with a first-order decay rate constant. The numerical results were in good agreement with the results of physical sandbox model and the analytical solution. The developed model was applied to two different studies, using multi-objective genetic algorithm to optimise remediation design. In order to reach the optimised design, three objectives considering three constraints were defined. The time to reach the desired concentration and remediation cost regarding the number of required oxidant sources in the optimised design was less than any arbitrary design.

Electrolytic groundwater circulation well for trichloroethylene degradation in a simulated aquifer

An in situ groundwater remediation process, termed EGCW, was developed in this study by integrating in-well groundwater electrolysis into groundwater circulation well. Groundwater circulation carries electrolytically generated O2 and H2 into the impacted aquifer for in situ biodegradation of contaminants. In a two-dimensional tank filled with field sandy sediments, simulated trichloroethylene (TCE)-contaminated groundwater was circulated between an injection well with electrodes inside and a pumping well. Results from a 50-day EGCW experiment show that in-well electrolysis oxygenated most region near the injection well, and 10 mg/L TCE was mainly biodegraded aerobically to about 2.7 mg/L (73% removal) by the indigenous microbes. Aerobic TCE degradation was enhanced by the pulsed addition of acetate. Together with the proofs of stable carbon isotope fractionation (enrichment factor: −0.57‰–−1.53‰) and microbial community variation after EGCW treatment, aerobic cometabolism was proposed to be the most likely mechanism for TCE degradation. It is interesting to find that the intrinsic organic carbon in aquifer matrix could fuel the aerobic TCE degradation, particularly at low TCE concentrations. EGCW treatment is advantageous in terms of supplying appropriate dosages of electron acceptor (O2) and donor (H2) for in situ bioremediation because groundwater electrolysis and circulation are expedient and controllable.

An adaptive dynamic surrogate model using a constrained trust region algorithm: application to DNAPL-contaminated-groundwater-remediation design

Simulation optimization is a robust tool in the design of groundwater remediation strategy. Simulation models for groundwater contaminated by dense non-aqueous phase liquids (DNAPLs) are usually computationally expensive, especially when used within a simulation optimization framework. Surrogate models have been proved to possess the potential to speed up complex models without sacrificing accuracy or detail. In this study, a constrained trust region (CTR)-based adaptive dynamic surrogate model was proposed and applied to a DNAPL-contaminated-groundwater-remediation-design scenario. First, the multiphase flow simulation model was constructed to simulate the groundwater remediation process. Then, preliminary input–output sample points were generated using the Latin hypercube sampling method and the developed simulation model. Next, the static Kriging surrogate model, as well as the nonlinear optimization model, were constructed. Finally, the surrogate model and optimization model were updated based on the CTR method and the optimal remediation strategy was obtained. The application of the CTR-based dynamic surrogate model reduced the mean relative error by 46% compared with the static surrogate model, and greatly improved the reliability of the optimal remediation strategy. The CTR-based dynamic surrogate model had the best fitting accuracy of multiphase flow simulation when compared with the expected improvement-based dynamic surrogate model and the optimal solution-based dynamic surrogate model. The corresponding optimization model also had the minimum remediation cost. This study demonstrates that the proposed CTR-based dynamic surrogate model is an effective tool to increase the accuracy of surrogate models and the reliability of their optimization results.

Experimental and numerical evaluation of Groundwater Circulation Wells as a remediation technology for persistent, low permeability contaminant source zones

Contaminants removal stoked inside low permeability zones of aquifers is one of the most important challenge of groundwater remediation process today. Low permeability layers can be considered persistent secondary sources of contamination because they release pollutants by molecular diffusion after primary source of contamination is reduced, causing long plum tails (Back-Diffusion). In this study, the Groundwater Circulation Well (GCW) system was investigated as an alternative remediation technology to the low efficient traditional pumping technologies to restore contaminated low permeability layers of aquifers. The GCW system creates vertical groundwater circulation cells by drawing groundwater through a screen of a multi-screen well and discharging it through another screen. The suitability of this technology to remediate contaminated low permeability zones was investigated by laboratory test and numerical simulations. The collected data were used to calibrate a model created to simulate the Back-Diffusion process and to evaluate the effect of different pumping technologies on the depletion time of that process. Results show that the efficiency of the GCW is dependent on the position and on the geometry of the low permeability zones, however the GCW system appears more suitable to restore contaminated low permeability layers of aquifers than the traditional pumping technology.

The Use of Nanoparticles in Soil and Water Remediation Processes

Environmental nanoremediation of various contaminants has been reported in several recent studies. In this paper, the state of the art on the use of nanoparticles in soil and groundwater remediation processes is presented. There is a substantive body of evidence on the growing and successful application of nanoremediation for a diversity of soil and groundwater contamination contexts, particularly, for heavy metals, other inorganic contaminants, organic contaminants and emerging contaminants, as pharmaceutical and personal care products. This review confirms the competence of the use of nanoparticles in the remediation of contaminated media and the prevalent use of iron based nanoparticles.

Control of Groundwater Remediation Process as Distributed Parameter System

Abstract Pollution of groundwater requires the implementation of appropriate solutions which can be deployed for several years. The case of local groundwater contamination and its subsequent spread may result in contamination of drinking water sources or other disasters. This publication aims to design and demonstrate control of pumping wells for a model task of groundwater remediation. The task consists of appropriately spaced soil with input parameters, pumping wells and control system. Model of controlled system is made in the program MODFLOW using the finitedifference method as distributed parameter system. Control problem is solved by DPS Blockset for MATLAB & Simulink.

Adaptive Kriging surrogate model for the optimization design of a dense non-aqueous phase liquid-contaminated groundwater remediation process

The optimization model needs to call the simulation model to calculate the response under different conditions for many times, and this is computationally expensive and time-consuming. To solve this problem, surrogate models can be used to yield insight into the functional relationship between the design variables and the responses, instead of simulation models in the optimization. In this paper, an integrated optimization method based on adaptive Kriging surrogate models was proposed and applied to the cost optimization of a surfactant enhanced aquifer remediation process for dense non-aqueous phase liquids (DNAPLs). First, the initial samples were created by Latin hypercube sampling, and then the responses corresponding to the initial samples were computed by a simulation model. The initial Kriging model was derived through these samples. Secondly, the adaptive Kriging surrogate model was proposed based on updating initial Kriging with new samples via infill sampling criteria. The results showed that it had improved the accuracy of the surrogate model, and the added samples had provided more information about the simulation model than the common samples. Even with the same number of samples, the adaptive Kriging surrogate model performed better than the common Kriging surrogate model, which was built only once. What's more, the integrated approach not only greatly reduced the computational burden, but also determined the actual optimal DNAPLs remediation strategy.

Comparison of surrogate models with different methods in groundwater remediation process

Surrogate modelling is an effective tool for reducing computational burden of simulation optimization. In this article, polynomial regression (PR), radial basis function artificial neural network (RBFANN), and kriging methods were compared for building surrogate models of a multiphase flow simulation model in a simplified nitrobenzene contaminated aquifer remediation problem. In the model accuracy analysis process, a 10-fold cross validation method was adopted to evaluate the approximation accuracy of the three surrogate models. The results demonstrated that: RBFANN surrogate model and kriging surrogate model had acceptable approximation accuracy, and further that kriging model’s approximation accuracy was slightly higher than RBFANN model. However, the PR model demonstrated unacceptably poor approximation accuracy. Therefore, the RBFANN and kriging surrogates were selected and used in the optimization process to identify the most cost-effective remediation strategy at a nitrobenzene-contaminated site. The optimal remediation costs obtained with the two surrogate-based optimization models were similar, and had similar computational burden. These two surrogate-based optimization models are efficient tools for optimal groundwater remediation strategy identification.

Anaerobic Reaction of Nanoscale Zerovalent Iron with Water: Mechanism and Kinetics

Nanoscale zerovalent iron (nZVI) is commonly used in advanced groundwater remediation processes. Here, we present a combined experimental and computational approach to elucidate the mechanism and kinetics of the reaction of nZVI with water under anaerobic conditions, which represents the basic reaction controlling the stability of nZVI in groundwater. The reaction kinetics was monitored at temperatures of 25 and 80 °C by 57Fe Mossbauer spectroscopy on frozen dispersion samples. The experimentally determined rate constant for reaction of nZVI with water at 25 °C was 1.14 × 10–3 h–1; the activation barrier measured for 60 nm sized nanoparticles (ΔG⧧298K(aq) = 26.3 kcal/mol) fits the range delineated by two limiting theoretical models from advanced quantum chemical calculations: rate-limiting activation barriers of 31.6 and 18.0 kcal/mol depending on the computational model, i.e., an iron atom and an infinite iron surface, respectively. The computations indicated a two-step reaction mechanism involving two o...

Sobol′ sensitivity analysis of NAPL-contaminated aquifer remediation process based on multiple surrogates

Sobol′ sensitivity analyses based on different surrogates were performed on a trichloroethylene (TCE)-contaminated aquifer to assess the sensitivity of the design variables of remediation duration, surfactant concentration and injection rates at four wells to remediation efficiency First, the surrogate models of a multi-phase flow simulation model were constructed by applying radial basis function artificial neural network (RBFANN) and Kriging methods, and the two models were then compared. Based on the developed surrogate models, the Sobol′ method was used to calculate the sensitivity indices of the design variables which affect the remediation efficiency. The coefficient of determination (R2) and the mean square error (MSE) of these two surrogate models demonstrated that both models had acceptable approximation accuracy, furthermore, the approximation accuracy of the Kriging model was slightly better than that of the RBFANN model. Sobol′ sensitivity analysis results demonstrated that the remediation duration was the most important variable influencing remediation efficiency, followed by rates of injection at wells 1 and 3, while rates of injection at wells 2 and 4 and the surfactant concentration had negligible influence on remediation efficiency. In addition, high-order sensitivity indices were all smaller than 0.01, which indicates that interaction effects of these six factors were practically insignificant. The proposed Sobol′ sensitivity analysis based on surrogate is an effective tool for calculating sensitivity indices, because it shows the relative contribution of the design variables (individuals and interactions) to the output performance variability with a limited number of runs of a computationally expensive simulation model. The sensitivity analysis results lay a foundation for the optimal groundwater remediation process optimization.

Carbo-Iron®—Synthesis and stabilization of Fe(0)-doped colloidal activated carbon for in situ groundwater treatment

Carbo-Iron – a novel composite material is presented, which consists of nanoscale zero-valent iron (nZVI) clusters on activated carbon colloids (ACC). The material is especially designed for the in situ generation of reactive zones and contaminant source removal when applied in groundwater remediation processes. This paper deals with the tailored synthesis of the composite material to create a reducing agent with properties optimized for subsurface transport of the particles and improved source affinity when compared to pure nZVI. Various iron precursors and impregnation methods are discussed with regard to their influence on the material properties. The impregnated ACCs were reduced by either H2 or C/CO in order to achieve the desired Carbo-Iron colloids. By means of thermogravimetric analysis the optimal reduction temperatures were determined for H2 (500°C) and for the carbon carrier material (700°C) as reducing agent. For stabilization of the initially generated pyrophoric highly dispersed iron nanoclusters various approaches were studied in order to generate an air-stable material without significant loss of metallic iron. Stabilization with traces of gaseous HCl gave the best results. Finally, the influence of the nZVI and residual iron oxide deposits on the sorption properties of the ACC carrier is discussed.

Electrogeneration of H2 for Pd-catalytic hydrodechlorination of 2,4-dichlorophenol in groundwater

A novel electrolytic groundwater remediation process, which used the H2 continuously generated at cathode to achieve in situ catalytic hydrodechlorination, was developed for the treatment of 2,4-dichlorophenol (2,4-DCP) in groundwater. Catalytic hydrodechlorination using Pd supported on bamboo charcoal and external H2 showed that 2,4-DCP was completely dechlorinated to phenol within 30min at pH⩽5.5. In a divided electrolytic system, the catalytic hydrodechlorination of 2,4-DCP in cathodic compartment by H2 generated at the cathode under 20 and 50mA reached 100% at 120 and 60min, respectively. Two column experiments with influent pHs of 5.5 (unconditioned) and 2 were conducted to evaluate the feasibility of this process. The 2,4-DCP removal efficiencies were about 63% and nearly 100% at influent pHs of 5.5 and 2, respectively. Phenol was solely produced by 2,4-DCP hydrodechlorination, and was subsequently degraded at the anode. A low pH could enhance the hydrodechlorination, but was not necessarily required. This study provides the preliminary results of a novel effective electrolytic process for the remediation of groundwater contaminated by chlorinated aromatics.

A stochastic optimization model under modeling uncertainty and parameter certainty for groundwater remediation design—Part I. Model development

Solving groundwater remediation optimization problems based on proxy simulators can usually yield optimal solutions differing from the “true” ones of the problem. This study presents a new stochastic optimization model under modeling uncertainty and parameter certainty (SOMUM) and the associated solution method for simultaneously addressing modeling uncertainty associated with simulator residuals and optimizing groundwater remediation processes. This is a new attempt different from the previous modeling efforts. The previous ones focused on addressing uncertainty in physical parameters (i.e. soil porosity) while this one aims to deal with uncertainty in mathematical simulator (arising from model residuals). Compared to the existing modeling approaches (i.e. only parameter uncertainty is considered), the model has the advantages of providing mean-variance analysis for contaminant concentrations, mitigating the effects of modeling uncertainties on optimal remediation strategies, offering confidence level of optimal remediation strategies to system designers, and reducing computational cost in optimization processes.

A Rotary Thermal Probe for Measuring Groundwater Velocity

A rotary thermal groundwater flowmeter was studied theoretically and experimentally, and its electrical model was used for searching a linear transference. The velocity measuring range was extended with respect to previous developments, and flow direction estimated by rotating the sensor head of the probe. This instrument was tested under laboratory conditions with the probe in contact with saturated sand and within standard plastic screens. Simulations suggested that the probe would work properly with high flow velocities arising in some groundwater remediation processes. Flow direction was estimated with a mean squared error of 1.3° and a standard deviation of 0.9°.

Modeling for Surfactant-Enhanced Groundwater Remediation Processes at DNAPLs-Contaminated Sites

In this study, a numerical modeling technique for simulating surfactant-enhanced groundwater remediation processes is introduced. The model could not only examine the physicochemical behaviour of a multiphase system due to surfactant injection through considering balance equations for all phases, but also explain the solubilization and mobilization mechanisms for removing entrapped residual DNAPL due to introduction of surfactant solutions. The model is applied to a hypothetical PCE-contaminated site for simulating the surfactant-flushing process. The application indicates that, given some approximate information of initial PCE distribution and layering, surfactant-enhanced remediation can be simulated by the model to a reasonable degree of certainty. The modeling results show that the surfactant has the significant potential to improve remediation efficiency, reduce remediation time, and lower down remediation costs in comparison with conventional pump-and-treat remediation measure. This study demonstrates the model's capability in simulating the remediation processes and effectiveness in interpreting surfactant enhancement effect. It is indicated that modeling studies could not only help gain insights of the remediation processes but also provide scientific bases for decision and design of site-specific remediation plans.