Dense Nonaqueous Phase Liquid Source Research Articles

Bidirectional machine learning-assisted sensitivity-based stochastic searching approach for groundwater DNAPL source characterization.

In this study, we designed a machine learning-based parallel global searching method using the Bayesian inversion framework for efficient identification of dense non-aqueous phase liquid (DNAPL) source characteristics and contaminant transport parameters in groundwater. Swarm intelligence organized hybrid-kernel extreme learning machine (SIO-HKELM) was proposed to approximate the forward and inverse input-output correlation with a high accuracy using the DNAPL transport numerical simulation model. An adaptive inverse-HKELM was established for preliminary estimation of the source characteristics and contaminant transport parameters to correct prior information and generate high-quality initial starting points of parallel searching. A local accurate forward-HKELM surrogate of the numerical model was embedded in the searching system for avoiding repetitive CPU-demanding likelihood evaluations. A sensitivity-based Metropolis criterion (MC), incorporating the dynamic particle swarm optimization (SD-PSO) algorithm, was developed for improving the search ergodicity and realizing precise inversion of all the unknown variables with drastic variations in sensitivity to the likelihood function. Results showed that the generalization capability and robustness of SIO-HKELM were superior to those of the traditional machine learning methods, including KELM and support vector regression (SVR), and it sufficiently approximated the forward and inverse input-output mapping of the numerical model with testing determination coefficients of 0.9944 and 0.6440, respectively. With high-quality prior information and initial starting points generated by the adaptive inverse-HKELM feed approach, the uncertainty in the inversion outputs was reduced, and the searching process rapidly converged to reasonable posterior distributions in around 60 iterations. Compared with the widely used multichain Markov chain Monte Carlo (MCMC) approach, the parallel searching lines generated by SD-PSO-MC adequately covered the searching space, and the "equifinality" effect was more effectively restrained by reducing the relative errors of all the point estimations to less than 8%. Therefore, the real source information reflected by the statistical characteristics of the SD-PSO-MC inversion outputs was more precise than that obtained using the multichain MCMC approach.

Improved ERT imaging with 3-D surface-to-horizontal borehole configurations: relevance to dense non-aqueous phase liquids

SUMMARY Accurate characterization and monitoring strategies are essential for designing and implementing remedial programs for sites polluted with dense non-aqueous phase liquids (DNAPLs). Electrical resistivity tomography (ERT) is a widely used geophysical technique for mapping subsurface features and processes of interest, and exhibits desirable characteristics for DNAPL sites due to its ability to gather large volumes of continuous subsurface information in a non-invasive, cost-effective and time-efficient manner. However, ERT measured only from the surface suffers from poor imaging quality with depth. Enhanced ERT imaging can be obtained via electrodes deployed on the surface and within horizontal boreholes, but so far it has only been investigated for 2-D imaging. This study evaluates the potential of 3-D surface-to-horizontal borehole (S2HB) ERT configurations for imaging 3-D DNAPL source zones. Laboratory tank experiments were first conducted with a 3-D S2HB ERT configuration, which consisted of a surface grid and a single borehole line of electrodes, being used to monitor DNAPL migration within porous media. Results demonstrate that 3-D S2HB ERT with a single borehole provides improved sensitivity at depth, and therefore enhanced imaging compared to conventional 3-D surface ERT. Further tank experiments were performed to assess the performance of single borehole S2HB ERT when (i) the distance between surface and borehole is increased, and (ii) additional horizontal boreholes are included. The S2HB ERT with a single borehole significantly outperforms surface ERT at larger depths, and performs comparably to S2HB ERT using multiple boreholes. This study suggests that 3-D S2HB ERT with a single borehole can provide the enhanced imaging ability needed to map DNAPLs, while also being relatively practical for implementation at field sites.

Rock core VOC profiles diagnostic of aquitard occurrence and integrity in a multi-layered sedimentary rock aquifer flow system

Aquitard assessment poses a challenge for practitioners and researchers managing dense non-aqueous phase liquid (DNAPL) contaminants in sedimentary rock aquifer systems. Within these systems, DNAPL migration and bulk hydraulic properties are controlled by fracture networks which are difficult to characterize at relevant scales with conventional methods. A previous study at a DNAPL contaminated site used contrasts in high-resolution vertical gradients to delineate four aquitards and two surfaces with poor vertical connectivity within a thick (>200 m) sedimentary rock aquifer system primarily composed of layered sandstones. In this study, we corroborate that method by using the existing DNAPL contamination as a tracer to identify and characterize the aquitards in the sequence and assess aquitard integrity. Closely spaced samples of rock from four continuous cores were collected and analyzed for volatile organic compounds (VOCs). The cores were collected along a transect perpendicular to groundwater flow and 75 m downgradient of the DNAPL source zone. Dissolved phase contaminant concentrations were collected using high-resolution multilevel systems placed along the plume centerline, downgradient from the source. The rock core and groundwater VOC concentration profiles suggest a primary aquitard limits vertical migration of both DNAPL and the associated dissolved phase contaminants, despite relatively large, downward gradients across its lower boundary. A calibrated, three-dimensional numerical groundwater flow model indicates an anisotropy factor of 1,000 for this aquitard. The Kv values for all four aquitards are only slightly lower than the values for the aquifers. However, the aquitard Kv values are 1–3 orders of magnitude less than their Kh values, and the aquitard Kh values are often similar to or even greater than the Kh values for the aquifers. Therefore, large anisotropy, rather than exceptionally low Kv values, distinguish the aquitards from the aquifers in this system. Fracture network characterization of nearby outcrops showed the anisotropy of the aquitards can be attributed to laterally extensive, bedding parallel partings coupled with short, frequently terminating, and poorly connected vertical fractures. In addition, the rock VOC profiles combined with fracture network data show that fracture termination surfaces can be powerful inhibitors of downward DNAPL migration providing additional evidence for the importance of these features in sedimentary rock flow systems. Ultimately, this study reveals the important influence of anisotropic aquitards on DNAPL migration, accumulation, and persistence, as well as associated plume migration. Furthermore, it demonstrates that subtle contrasts critical to aquitard integrity may be easily missed with long screened wells and other conventional, low resolution site characterization methods.

Characterization of DNAPL source zones in clay-sand media via joint inversion of DC resistivity, induced polarization and borehole data

Toxic organic contaminants in groundwater are pervasive at many industrial sites worldwide. These contaminants, such as chlorinated solvents, often appear as dense non-aqueous phase liquids (DNAPLs). To design efficient remediation strategies, detailed characterization of DNAPL Source Zone Architecture (SZA) is required. Since invasive borehole-based investigations suffer from limited spatial coverage, a non-intrusive geophysical method, direct current (DC) resistivity, has been applied to image the DNAPL distribution; however, in clay-sand environments, the ability of DC resistivity for DNAPLs imaging is limited since it cannot separate between DNAPLs and surrounding clay-sand soils. Moreover, the simplified parameterization of conventional inversion approaches cannot preserve physically realistic patterns of SZAs, and tends to smooth out any sharp spatial variations. In this paper, the induced polarization (IP) technique is combined with DC resistivity (DCIP) to provide plausible DNAPL characterization in clay-sand environments. Using petrophysical models, the DCIP data is utilized to provide tomograms of the DNAPL saturation (SN) and hydraulic conductivity (K). The DCIP-estimated K/SN tomograms are then integrated with borehole measurements in a deep learning-based joint inversion framework to accurately parameterize the highly irregular SZA and provide a refined DNAPL image. To evaluate the performance of the proposed approach, we conducted numerical experiments in a heterogeneous clay-sand aquifer with a complex SZA. Results demonstrate the standalone DC resistivity method fails to infer the DNAPL in complex clay-sand environments. In contrast, the combined DCIP technique provides the necessary information to reconstruct the large-scale features of K/SN fields, while integrating DCIP data with sparse but accurate borehole data results in a high resolution characterization of the SZA.

Numerical investigations of influence on thermal conductive heating in DNAPL-impacted soils by heterogeneity

The treatment of dense non-aqueous phase liquid (DNAPL) source zones is challenging, particularly in heterogeneous media. Thermal conductive heating (TCH) has the potential to treat DNAPL source zones for its ability to enhance mass transfer. This study uses numerical modeling combined with Monte Carlo simulation to examine the influence of heterogeneity of the permeability field on the DNAPL distribution and TCH remediation. It is found that the longer horizontal correlation length of high-permeable clay lens would contribute to accelerate multiphase convection and then heat conduction in horizontal direction. The presence of DNAPL (both in NAPL phase and dissolved), pressure, and permeability are all contributory factors to phase change. Realizations with shorter horizontal correlation lengths always have a larger vertical spreading of DNAPL. As a shallow vapor extraction well was applied in this study, higher TCE removal rate can be obtained in the realizations with a shorter horizontal correlation length when NAPL phase of TCE is existed in the domain.

Bayesian ensemble machine learning-assisted deterministic and stochastic groundwater DNAPL source inversion with a homotopy-based progressive search mechanism

A hyperheuristic homotopy algorithm (HH-HA) and a homotopy-based swarm intelligence algorithm for parallel stochastic search are proposed to improve the search ergodicity and stability of deterministic and stochastic inversion approaches, respectively, for the identification of dense nonaqueous phase liquid (DNAPL) source characteristics and contaminant transport parameters in groundwater. Meanwhile, a Bayesian ensemble machine learning (BEML) method for numerical simulation surrogate modeling is proposed to enhance the learning and generalization capability, while significantly reducing the computational cost of repetitive CPU-demanding likelihood evaluations in inversion iterations. Two cases are presented for the verification and application of the proposed inverse modeling methods. The results show that the BEML surrogate model is powerful in approximating the numerical model outputs, reducing the mean relative errors of the contaminant concentration and pressure head predictions to 1.18% and 1.29%, respectively. The homotopy-based progressive search mechanism is highly effective in achieving more accurate identification values for deterministic inversion and a more reliable posterior distribution for stochastic inversion. The HH-HA shows stable performance, approaching the global optimum in wide areas and reducing the mean relative error of identification from 11.95% to 4.11%. The homotopy-based parallel stochastic search lines adequately cover the search space tracking the change of homotopy parameter, and the mean relative error of point estimation is reduced to 2.03%. Though the deterministic inversion approach can provide intuitive identification values, the reliability of the inversion outputs cannot be quantified. In contrast, the proposed stochastic inversion system is of great significance for providing decision makers with comprehensive reference information.

Mass-flux Reduction and Mass Removal Relationships (MFR-MR) of Pool-dominated Multicomponent DNAPLs in Heterogeneous Geological System

<p>Contamination of groundwater by chlorinated solvents such as trichloroethylene (TCE, tetrachloroethylene (PCE), and 1,1-dichloroethylene (1,1DCE) is a widespread problem that can seriously threaten human health and the environment. These compounds are often present as dense nonaqueous phase liquid (DNAPL) mixtures in the subsurface that serve as a long-term groundwater contamination sources. The objective of this study is to test the effectiveness of different in-situ flushing agents (SDS, Tween 80, MCD, and water) for the recovery of DNAPL mixtures (TCE, PCE, 1,1DCE) entrapped in heterogeneous aquifer media. A series of batch tests were first performed to assess the multiphase behavior of water-DNAPL mixture-reagent system. Intermediate scale tank experiments were then performed to compare the performance of the reagent solutions. The flushing breakthrough curves were also used to investigate the impact of solubilization agents on mass-flux reduction/mass-removal behavior relationships. Performance of agents on DNAPL mixture source zone remediation was found to be in the following order: Tween 80>SDS>MCD>>Water. Individual DNAPL constituents exhibited transient effective solubilities that depend on the reagent contents and DNAPL compositions. Although high fractions of DNAPL source zones in heterogeneous porous media were removed through chemical flushing (SDS, Tween 80, MCD, and water), the heterogeneous system exhibited an extended multi-step concentration behavior. The results emphasized that in the early stage, some portion of the organic liquid is hydraulically accessible (matrix) whereas the later stage of mass removal was controlled by the more poorly accessible mass (pool) associated with higher-saturation zones. Our results also showed that the distribution and the emplacement of organic liquid, flow-field heterogeneity, sorption and desorption, and flushing solution all influenced the DNAPL mixture removal. The results from this study underline the need for understanding the multiphase behaviour of a system, particularly when DNAPL mixtures are present, prior to field-scale remediation implementation.</p>

An approach for quantification of the heterogeneity of DNAPL source zone geometries

Many studies have investigated the migration and entrapment processes of source zones from dense non-aqueous phase liquid (DNAPL) contamination under different conditions. However, the characterization of occupying area by source zone (or source shape) in water-saturated aquifers is still rudimentarily considered. In this study, we demonstrated this issue (1) by providing a brief review of existing approaches for source shape consideration, (2) by proposing an approach with simple shape parameters based on the non-uniformity of source widths, and (3) by providing exemplary applications of our proposed approach on shapes already published in previous research works. Our literature review suggested that the source zone in mathematical approaches is generally characterized as simple geometrical shapes (arbitrary lines or rectangles) or system-defined parameters that contrast to complex and discontinuous shapes observed in the real world. But the characterization of such complex shapes is still not possible with acceptable efforts. Therefore, we proposed an approach to parameterize the source shape by considering the variation of width and midpoints over the depth of the entire source zone and formulate four parameters based on population statistics (mean, standard deviation). To illustrate the suitability of our approach, we applied it to the results of lab experiments, and by analyzing these complex shapes, we highlighted the potential for improving the characterization techniques of non-uniformity of the source zones.

Virtual Site Investigation to Evaluate Conceptual Site Model Development at DNAPL‐Impacted Sites

AbstractThe development of a conceptual site model (CSM) is essential to support decision making for the investigation and remediation of contaminated sites, including sites impacted by dense nonaqueous phase liquids (DNAPLs). Dynamic investigation strategies using high‐resolution site investigation tools can be used to increase the accuracy, and increase the precision (reduce the uncertainty) of a CSM, while balancing life‐cycle costs. However, the evaluation of CSM accuracy is limited by the hydrogeological and biogeochemical complexities inherent in subsurface systems. This study simulated DNAPL migration, dissolution, and reactive transport to create three high‐resolution “virtual sites” composed of 0.4–1 billion parameter values each, which were investigated by teams of leading practitioners. Use of these virtual sites allowed key hydrogeological and biogeochemical parameters to be known by the model developers but not the investigation teams. Comparison of the CSMs developed by these practitioners to the simulated values across multiple metrics (e.g., dissolved concentrations, DNAPL mass, and mass discharge) showed that 92% of those metrics were estimated within a factor of 10 and 63% were within a factor of 3 of the true (simulated) values. Furthermore, estimates of dissolved phase plume footprint were often more accurate than estimates of DNAPL source footprint, and the approaches used for investigation and data analysis were both important for CSM development. Overall, none of the site investigation approaches used to develop the CSMs resulted in the highest accuracy for all metrics, which demonstrates the need to establish best‐practices that draw from multiple approaches and vary depending on investigation objectives.

Deep learning based optimization under uncertainty for surfactant-enhanced DNAPL remediation in highly heterogeneous aquifers

The optimization for surfactant-enhanced aquifer remediation (SEAR) of dense non-aqueous phase liquid (DNAPL)-contaminated aquifers is usually accompanied by uncertainties, which may arise from the characterization of complex aquifer heterogeneity and DNAPL source zone architecture (SZA) due to measurement sparsity. Optimization under uncertainty is computationally expensive as it involves an enormous number of model runs. The huge computational burden can be alleviated by utilizing a surrogate model for repeated model evaluations. However, most of the developed surrogates are often limited to low-dimensional optimization problems that only consider simplified aquifer heterogeneity. In this study, we developed a multi-objective simulation-optimization framework to optimize the SEAR schemes considering the characterization uncertainties from both highly heterogeneous aquifer permeability and complex SZA. A fast-to-run convolutional neural network (CNN)-based surrogate model was developed to approximate the high-dimensional and highly complex input-output mapping of the DNAPL multiphase flow simulation model. We first used the rejection sampling strategy to generate random realizations of permeability and SZA conditioning on their limited measurements and then formulated a multi-objective optimization under uncertainty problem based on these realizations. The developed 3-D CNN was trained and used as the surrogate for repeated model runs in optimization to identify the optimal SEAR schemes under uncertainty. A 3-D numerical experiment was used to test the performance of the CNN-based simulation-optimization framework. Comprehensive analysis on the obtained Pareto fronts demonstrates that the proposed framework can efficiently identify reliable Pareto-optimal solutions with a 99.8% speedup compared to the traditional optimization coupled with the forward model. Moreover, the optimization considering multiple realizations enables us to perform the risk assessment to locate the risk zone where the NAPL phase possibly exists after remediation, which provides useful information for decision-making.

Enhanced-solubilization and dissolution of multicomponent DNAPL from homogeneous porous media

The presence of multicomponent dense nonaqueous phase liquid (DNAPL) mixtures in porous media can significantly limit the effectiveness of groundwater remediation. A series of column transport and flushing experiments were conducted to quantify the impact of various enhanced flushing agents on dissolution and removal of a multicomponent DNAPL source within a macroscopically homogeneous porous medium. The columns were established with NAPL saturations consisting of an equal mole mixture of cis-1,2-dichloroethene, trichloroethene, and tetrachloroethene. The solubilization agents used included two complexing sugars - hydroxypropyl-β-cyclodextrin (HPCD and methyl-β-cyclodextrin (MCD); a surfactant - sodium dodecyl sulfate (SDS); and a cosolvent - ethanol (EtOH). The chemical flushing agents greatly reduced the time needed to remove each DNAPL component, compared to flushing with water alone. Initial DNAPL-component elution concentrations were successfully predicted using Raoult's Law for MCD, HPCD, and water flushing, indicating that ideal dissolution was initiated by the lower-power enhanced-solubilization agents. EtOH was most efficient at removing the contaminants in terms of normalized mass recovery but least efficient based on a mass-ratio and mole-ratio of contaminant to reagent analysis. SDS was most efficient for contaminant removal when analyzed based on mass-contaminant to mass-reagent recovered and MCD was most effective based on a moles-contaminant to moles-reagent recovered efficiency evaluation. However, in terms of mass flux reduction analysis (i.e. removal metric), SDS was least efficient for contaminant removal compared to all other enhanced-flushing agents tested, especially during the initial stages of DNAPL removal. Results from this study indicate that ideal dissolution was initiated during enhanced-solubilization and that several criteria should be used to evaluate the removal effectiveness of flushing agents for multi-component NAPL systems.

Mapping and Monitoring of DNAPL Source Zones With Combined Direct Current Resistivity and Induced Polarization: A Field‐Scale Numerical Investigation

AbstractDirect current (DC) resistivity has been widely investigated for non‐invasive mapping of dense non‐aqueous phase liquids (DNAPLs); however, due to its difficulty in distinguishing DNAPLs from adjacent soils, the DC method is limited for static detection of DNAPLs and more often employed for monitoring DNAPL changes over time. Time‐domain induced polarization (IP) can provide complementary information to better discriminate between DNAPL, water and surrounding soils. Since highly resistive DNAPLs tend to laterally spread and pool on polarizable (chargeable) clay lenses, combined DC and IP (DCIP) has the potential to enhance static detection, and also monitoring of DNAPL source zones (SZs). The objective of this study is to assess DCIP for characterizing and monitoring DNAPL SZs at the field‐scale. A new DNAPL‐DCIP numerical model was first developed that couples a 3D multiphase flow model, which simulates DNAPL release and remediation scenarios, with a 3D DCIP model, which calculates the corresponding resistivity and chargeability response. The sensitivity of the DCIP response to key DNAPL and soil properties was then analyzed at a single subsurface location, closely matching previous theoretical and experimental observations. Finally, a field‐scale simulation of DNAPL release and remediation was conducted, with simultaneous mapping by DCIP surveys. Results demonstrate that chargeability can provide enhanced understanding of the lithological distribution that controls the variability in the DNAPL SZ, with time‐lapse resistivity being used to monitor DNAPL mass changes during SZ remediation. This numerical study suggests that combined DCIP can be valuable for site characterization and time‐lapse monitoring performance at DNAPL‐impacted sites.

Evaluation of the benefits of improved permeability estimation on high-resolution characterization of DNAPL distribution in aquifers with low-permeability lenses

High-resolution characterization of dense non-aqueous phase liquid (DNAPL) source zone architecture (SZA) is crucial for developing remediation strategies. Inverse modeling approaches are widely used to characterize DNAPL SZA. However, unknown parameters (permeability and DNAPL saturation) are usually assumed as Gaussian distribution in traditional geostatistical inverse methods (e.g., ensemble smoother with multiple data assimilation, ESMDA). This assumption may not be true in channels of fluvial deposits. The presence of clay lenses will bring significant challenges in reconstructing the DNAPL SZA using geostatistical inverse methods based on the Gaussian assumption. Moreover, the DNAPL characterization also suffers from a limited number of borehole data in the field. In this study, we use ESMDA's variant, ensemble smoother with multiple data assimilation-direct sampling (ESMDA-DS), to improve the estimation of non-Gaussian permeability field and evaluate the benefits of improved permeability estimation on the reconstruction of DNAPL source zone. The inversion technique is based on the iterative inversion framework to incorporate the clay content information into the DNAPL characterization. The performance of our proposed framework is examined and discussed through a numerical synthetic case with challenging pool-dominated SZA. To overcome data scarcity, we integrated hydrogeological and geophysical (electrical resistance tomography) datasets. Results show that the permeability is characterized well using ESMDA-DS method compared with ESMDA method. With the improved permeability estimation, the DNAPL source zone can be characterized in higher resolution. Compared with ESMDA method, the root mean square error (RMSE) of permeability is reduced by 38.5% using the ESMDA-DS method. Then the estimation RMSE of DNAPL saturation is reduced by 24.9% by improved permeability estimation. We also demonstrated the significant benefits of improved permeability estimation on DNAPL depletion behavior and plume evolution. Numerical simulations demonstrate with the improved permeability estimation, the DNAPL depletion prediction is highly improved, the prediction RMSE of the DNAPL dissolved plume is reduced by 34.5% after 50 years.

Integrating deep learning-based data assimilation and hydrogeophysical data for improved monitoring of DNAPL source zones during remediation

Data assimilation techniques allow the integration of multi-source data with physical modeling of dense nonaqueous phase liquid (DNAPL) source zones to monitor their evolution in time and thereby improve remediation efforts. The DNAPL source zone is governed by multiphase physics and its architecture is usually highly irregular and nonstationary. Traditional data assimilation methods use stationary models to describe these nonstationary DNAPL saturation fields, leading to significant errors in the estimation of unknown source zone architectures (SZAs). In this study, we propose a geostatistical data assimilation method to improve performance monitoring of DNAPL remediation. We estimate the DNAPL SZA after parameterizing the DNAPL saturation field using a deep learning method instead of a stationary statistical model. First, we train a convolutional variational autoencoder (CVAE) network to capture the physics of DNAPL infiltration and depletion. Then, we incorporate the CVAE network within the Ensemble Kalman filter (EnKF) to resolve the evolving DNAPL SZA by assimilating time-lapse hydrogeophysical measurements (i.e., oscillatory hydraulic tomography, downgradient dissolved DNAPL concentration, and electrical resistivity tomography). To assess the proposed CVAE-EnKF framework's performance, we conducted numerical experiments with evolving DNAPL SZAs in a 2D heterogeneous aquifer. Results show that the proposed framework significantly improved DNAPL saturation estimates throughout the source zone over time, compared to the standard EnKF method. CVAE-EnKF reduced the estimation error of DNAPL mass remediated by 51% from EnKF, along with better predictions of source zone longevity. It also better captured the morphology and center/spread of mass of the evolving remediated portions of the source zone and provided better estimates of pooled-DNAPL mass. Furthermore, the CVAE-EnKF method is more stable and converges faster than EnKF alone. Overall, the proposed data assimilation framework can improve real-time monitoring of DNAPL remediation and risk analysis.

Integrating hydraulic tomography, electrical resistivity tomography, and partitioning interwell tracer test datasets to improve identification of pool-dominated DNAPL source zone architecture

High-resolution characterization of complex dense non-aqueous phase liquid (DNAPL) contaminated sites is crucial for developing effective remediation strategies. The DNAPL source zone is usually characterized by hydraulic/partitioning tracer tomography (HPTT). However, the HPTT method may fail to capture the highly saturated pool-dominated DNAPL source zone architecture (SZA), because partitioning tracers tend to bypass the low-permeability zones where the pool DNAPL accumulates, resulting in a low-resolution DNAPL estimation. With a limited number of measurements, the estimation errors may be significant. To overcome these difficulties, time-lapse electrical resistivity tomography (ERT) was integrated with the partitioning interwell tracer test (PITT) and hydraulic tomography (HT) to characterize the pool-dominated DNAPL SZA. Herein, we proposed an iterative joint inversion framework coupling the multiphase flow model with the ERT forward model to estimate the heterogeneous permeability distribution and DNAPL SZA. Under this framework, permeability was estimated using the hydraulic head data from HT in stage 1, and the DNAPL SZA was subsequently estimated by assimilating both the PITT and ERT observations in stage 2. The permeability estimated from stage 1 was used as prior information for stage 2, and the DNAPL saturation estimated from stage 2 was served as prior information for stage 1 in the next loop to form an iterative loop to improve the estimation of both permeability and DNAPL SZA. The iterative joint inversion framework was evaluated in two numerical experiments with different heterogeneous structures by assimilating multi-source datasets, including hydraulic head, partitioning interwell tracer concentration, and electrical resistivity. Results show that with limited measurements of HPTT method, one can roughly capture the DNAPL distribution, missing the fine structure of the DNAPL SZA. In contrast, by incorporating multi-source datasets, the heterogeneous permeability and DNAPL SZA can be reconstructed with a higher resolution. Furthermore, the inversion accuracy of the DNAPL SZA improves progressively as the iteration proceeds, which demonstrates the advantage of utilizing complementary information from permeability and DNAPL distribution through the iteration framework. Comparing with the results without loop iteration, the estimation error is reduced by 17.3% for permeability and 8.6% for DNAPL saturation in Experiment 1; by 14.7% for permeability and 11.2% for DNAPL saturation in Experiment 2 through the iterative framework. To further evaluate our framework, we preformed the prediction of the depletion process of the DNAPL source zone and plume based on the estimated DNAPL SZA. Results show that using the iterative framework, the prediction of the SZA depletion is greatly improved, i.e., the estimation error of the dissolved downstream plume from the DNAPL source zone after 3 years is reduced by 20.9% in Experiment 1, and by 43.2% in Experiment 2, respectively, through the iterative framework. This significant improvement is because the iterative method can better capture the spread of DNAPL pool.

Hydrogeophysical Characterization of Nonstationary DNAPL Source Zones by Integrating a Convolutional Variational Autoencoder and Ensemble Smoother

AbstractDetailed characterization of dense nonaqueous phase liquid (DNAPL) source zone architecture (SZA) is essential for designing efficient remediation strategies. However, it is difficult to characterize a highly irregular and localized SZA, because traditional drilling investigations provide limited information. With limited data, the estimation accuracy of traditional geostatistical methods is strongly affected by the parameterization of the prior description of the SZA. To improve characterization performance, we parameterized the DNAPL saturation field using a physics‐based approach. We trained a convolutional variational autoencoder (CVAE) using data from multiphase modeling that captures the physics of DNAPL infiltration. The trained CVAE network was used in SZA inversion to obtain an improved prior DNAPL saturation field, instead of the typical stationary prior covariances. We then integrated the CVAE network into an iterative ensemble smoother (ES), to formulate a joint inversion framework. To overcome difficulties from limited/sparse data, we incorporated hydrogeological and geophysical datasets in the proposed inversion framework. To evaluate the performance of our method, we conducted numerical experiments in a hypothetical heterogeneous aquifer with an intricate SZA. The results show that the CVAE was an effective and efficient parameterization method which can capture the DNAPL infiltration patterns better than a Gaussian prior. The improved prior, combined with multisource datasets, can result in better resolution, and overall improved SZA characterization. In contrast to the standard ES method, the proposed framework reconstructed the SZA more accurately. We also demonstrated that DNAPL depletion behavior and dissolved concentration profiles can be predicted accurately using the estimated SZA.

Towards predicting DNAPL source zone formation to improve plume assessment: Using robust laboratory and numerical experiments to evaluate the relevance of retention curve characteristics

We conducted multiple laboratory trials in a robust and repeatable experimental layout to study dense non-aqueous phase liquid (DNAPL) source zone formation. We extended an image processing and analysis framework to derive DNAPL saturation distributions from reflective optical imaging data, with volume balance deviations < 5.07%. We used a multiphase flow model to simulate source zone formation in a Monte Carlo approach, where the parameter space was defined by the variation of retention curve parameters. Integral and geometric measures were used to characterize the source zones and implemented into a multi-criteria objective function. The latter showed good agreement between observation data and simulation results for effective DNAPL saturation values > 0.04, especially for early stages of DNAPL migration. The common hypothesis that parameters defining the DNAPL-water retention curves are constant over time was not confirmed. Once DNAPL pooling started, the optimal fit in the parameter space was significantly different compared to the earlier DNAPL migration stages. We suspect more complex processes (e.g., capillary hysteresis, adsorption) to become relevant during pool formation. Our results reveal deficits in the grayscale-DNAPL saturation relationship definition and laboratory estimation of DNAPL-water retention curve parameters to overcome current limitations to describe DNAPL source zone formation.

Improved Characterization of DNAPL Source Zones via Sequential Hydrogeophysical Inversion of Hydraulic‐Head, Self‐Potential and Partitioning Tracer Data

AbstractHigh‐resolution characterization of hydraulic properties and dense nonaqueous phase liquid (DNAPL) contaminant source is crucial to develop efficient remediation strategies. However, DNAPL characterization suffers from a limited number of borehole data in the field, resulting in a low‐resolution estimation. Moreover, high‐resolution DNAPL characterization requires a large number of unknowns to be estimated, presenting a computational bottleneck. In this paper, a low‐cost geophysical approach, the self‐potential method, is used as additional information for hydraulic properties characterization. Joint inversion of hydraulic head and self‐potential measurements is proposed to improve hydraulic conductivity estimation, which is then used to characterize the DNAPL saturation distribution by inverting partitioning tracer measurements. The computational barrier is overcome by (a) solving the inversion by the principal component geostatistical approach, in which the covariance matrix is replaced by a low‐rank approximation, thus reducing the number of forward model runs; (b) using temporal moments of concentrations instead of individual concentration data points for faster forward simulations. To assess the ability of the proposed approach, numerical experiments are conducted in a 3‐D aquifer with 104 unknown hydraulic conductivities and DNAPL saturations. Results show that with realistic DNAPL sources and a limited number of hydraulic heads, the traditional hydraulic/partitioning tracer tomography roughly reconstructs subsurface heterogeneity but fails to resolve the DNAPL distribution. By adding self‐potential data, the error is reduced by 24% in hydraulic conductivity estimation and 68% in DNAPL saturation characterization. The proposed sequential inversion framework utilizes the complementary information from multi‐source hydrogeophysical data sets, and can provide high‐resolution characterizations for realistic DNAPL sources.

The dissolution and microbial degradation of mobile aromatic hydrocarbons from a Pintsch gas tar DNAPL source zone

Source zones containing tar, a dense non-aqueous phase liquid (DNAPL), can contaminate groundwater for centuries. A common occurrence of tar is at former Pintsch gas factories. Little is known about the composition and fate of contaminants dissolving from Pintsch gas tar DNAPL. In this study, we determined the composition and water-soluble characteristics of mobile aromatic hydrocarbons and their biodegradation metabolites in the DNAPL contaminated groundwater at a former Pintsch gas tar plant. We assessed the factors that determine the fate of observed groundwater contaminants. Measured values of density (1.03–1.06 kg/m3) and viscosity (18.6–39.4 cP) were found to be relatively low compared to common coal tars. Analysis showed that unlike common coal tars phenanthrene is the primary component rather than naphthalene. Moreover, it was found that Pintsch gas tar contains a relatively high amount of light molecular aromatic hydrocarbon compounds, such as benzene, toluene, ethylbenzene and xylenes (BTEX). Less commonly reported components, such as styrene, ethyltoluenes, di-ethylbenzene, 1,2,4,5-tetramethylbenzene, were also detected in water extracts from Pintsch gas tar. Moreover, 46 relatively hydrophilic metabolites were found within the tar samples. Metabolites present within the tar suggest biodegradation of mobile aromatic Pintsch gas tar compounds occurred near the DNAPL. Based on eleven detected suspect metabolites, a novel anaerobic biodegradation pathway is proposed for indene. Overall, our findings indicate that Pintsch gas tar has higher invasive and higher flux properties than most coal tars due to its relatively low density, low viscosity and, high content of water-soluble compounds. The partitioning of contaminants from multi-component DNAPL into the aqueous phase and re-dissolution of their slightly less hydrophobic metabolites back from the aqueous phase into the DNAPL is feasible and demonstrates the complexity of assessing degradation processes within a source zone.

Application of spectral induced polarization for characterizing surfactant-enhanced DNAPL remediation in laboratory column experiments

The widespread presence of entrapped dense non-aqueous phase liquid (DNAPL) in the subsurface poses a continuing challenge to groundwater remediation. Cost-effective and high-resolution subsurface characterization is a critical issue for further DNAPL recovery due to the complexity of DNAPL source zone architecture (SZA). Geophysical techniques provide a noninvasive, spatially continuous and cost-effective way for monitoring the DNAPL remediation process. In particular, the spectral induced polarization (SIP) method has shown great potential in environmental problems. In this study, we performed real-time SIP measurements on DNAPL contaminated soil in columns to quantitatively assess the ability of SIP method for monitoring surfactant-enhanced DNAPL remediation process. Chemical data was simultaneously collected during the remediation process to verify the results obtained by SIP method. Taking account into the variations of subsurface environment, we conducted a series of column flushing experiments under different flow rate, surfactant concentrations and fluid salinities. The results highlight that SIP method is able to effectively monitor the DNAPL remediation process, as well as to evaluate the remediation efficiency under different conditions. The variations in the flow rate, the concentration of surfactant and the salinity of pore water not only affect remediation effectiveness, but also have an impact on the SIP signatures. This study shows that SIP performs better for monitoring DNAPL remediation at a relatively low flow rate of ~ 0.4 m/d, low surfactant concentration of 5000 mg/L and high salinity of 1.0 S/m, with an error of saturation estimation (RMSES) <0.1.