Unknown Contaminant Sources Research Articles

Identification of contaminant source and hydraulic conductivity field based on an ILUES-SOM surrogate model

Contaminant source identification and hydraulic conductivity estimation are of great significance for contaminant transport model in the subsurface media, but their actual values are difficult to obtain and can usually be inversely identified and estimated by sparse observations. In order to reduce computational cost in the process of estimating groundwater model parameters, the surrogate model was often used. This study addresses this challenge by proposing a modified self-organizing map (SOM) based surrogate model, named ILUES-SOM. The proposed model combines a modified iterative ensemble smoother method (SGSIM-ILUES) and the SOM algorithm to simultaneously identify contaminant source parameters and hydraulic conductivity field. Considering the characteristics of the proposed method (ILUES-SOM), a comparison of parameter estimation accuracy and computational efficiency is performed with the original SOM and SGSIM-ILUES inversion model. Moreover, the robustness of ILUES-SOM model for inversion was illustrated by proposing varying degrees of observation errors and missing early observation data. The results indicated that ILUES-SOM model can successfully retrieve unknown contaminant source simultaneously with heterogeneity hydraulic conductivity field in the groundwater system.

Joint identification of contaminant source based on the ensemble Kalman filter integrated with relation coefficient

To reduce the adverse influence of sudden water pollution accidents, it is essential to estimate the unknown contaminant source information (normally including the source location, initial release time, and total release mass) as soon as possible. The ensemble Kalman filter (EnKF) has been proven to be an effective algorithm for such an inverse problem. This paper proposes a new method based on EnKF to identify contaminant source information. The method we called RC-EnKF uses the relation coefficient of concentration instead of timely concentration as a state variable in the assimilation process. The advantage lies in the release source mass that can be decoupled from the parameter group of unknown contaminant information to improve the assimilation speed and reduce interference with the accuracy of assimilation. Two categories of cases are employed for validating the applicability and testing the performance compared with the traditional EnKF method in detail. Sensitivity analyses are carried out with different observation errors, number of observation sites, number of ensemble realizations, and model grid size. The results demonstrate that with the uncertain error of observation data, the RC-EnKF works nicely and shows superiority to the traditional EnKF method reflected in the strong immunity to interference from observation data errors and elevated efficiency with the requirement of fewer observation sites, ensemble realizations, and model grids. It illustrates that the RC-EnKF is a more efficient and robust method for estimating unknown contaminant source information.

Groundwater contaminant source identification based on an ensemble learning search framework associated with an auto xgboost surrogate

Groundwater contaminant source identification (GCSI) is commonly accompanied by search process which tweaks the unknown contaminant source information to match the simulation model outputs with the measurements. When solving identification task, search accuracy and time cost have always been challenges that must be tackled. In the present study, a novel ensemble learning search framework associated with auto extreme gradient boosting tree (xgboost) was proposed to solve GCSI. In particular, auto xgboost was employed to reduce the calculation burden caused by repeatedly running simulation model. To promote search efficiency, boosting strategy (BOS) was employed to sequentially concatenate iterative ensemble smoother, differential evolution particle filter (DEPF), and swarm evolution algorithm. The identification results indicated that: 1. Auto xgboost could substitute a numerical simulation model with desired accuracy and expeditious running speed. 2. BOS could achieve better search accuracy, but with the sacrifice of infinitesimal calculated time cost, when compared with bagging strategy.

A direct parallel-in-time quasi-boundary value method for inverse space-dependent source problems

Inverse source problems arise often in real-world applications, such as localizing unknown groundwater contaminant sources. Being different from Tikhonov regularization, the quasi-boundary value method has been studied as an effective way for regularizing such inverse source problems, which was shown to achieve an optimal order convergence under suitable assumptions. However, fast direct or iterative solvers for the resulting large-scale all-at-once linear systems have been rarely studied in the literature. In this work, we propose and analyze a modified quasi-boundary value method that leads to a fast diagonalization-based parallel-in-time (PinT) direct solver, which can achieve a dramatic speedup in CPU times when compared with MATLAB’s sparse direct solver. In particular, the time-discretization matrix B is shown to be diagonalizable, and the condition number of its eigenvector matrix V is proven to exhibit only quadratic growth, which guarantees that the roundoff errors due to diagonalization is well-conditioned. Several 1D and 2D examples are presented to demonstrate the very promising computational efficiency of our proposed method, where the CPU times in 2D cases can be speeded up by three orders of magnitude.

An Integrated Bayesian and Machine Learning Approach Application to Identification of Groundwater Contamination Source Parameters

The identification of groundwater contamination source parameters is an important prerequisite for the control and risk assessment of groundwater contamination. This study developed an innovative approach for the optimal design of observation well locations and the high-precision identification of groundwater contamination source parameters. The approach involves Bayesian theory and integrates Markov Chain Monte Carlo, Bayesian design, information entropy, machine learning, and surrogate modeling. The optimal observation well locations are determined by information entropy, which is adopted to mine valuable information about unknown groundwater contamination source parameters from measurements of contaminant concentration according to Bayesian design. After determining the optimal observation well locations, the identification of groundwater contamination source parameters is implemented through a Bayesian-based Differential Evolution Adaptive Metropolis with Discrete Sampling–Markov Chain Monte Carlo approach. However, the processes of both determination and identification are time-consuming because the original simulation model (that is, the contaminant transport model) needs to be invoked multiple times. To overcome this challenge, a machine learning approach, that is, Multi-layer Perceptron, is used to build a surrogate model for the original simulation model, which can greatly accelerate the determination and identification processes. Finally, two hypothetical numerical case studies involving homogeneous and heterogeneous cases are used to verify the performance of the proposed approach. The results show that the optimal design of observation well locations and high-precision identification of groundwater contamination source parameters can be implemented accurately and effectively by using the proposed approach. In summary, this study highlights that the integrated Bayesian and machine learning approach provides a promising solution for high-precision identification of groundwater contamination source parameters.

Integrated Sequential Groundwater Contaminant Source Characterization and Pareto-Optimal Monitoring Network Design Application for a Contaminated Aquifer Site

Accurate and reliable groundwater contaminant source characterization with limited contaminant concentration monitoring measurement data remains a challenging problem. This study presents an illustrative application of developed methodologies to a real-life contaminated aquifer. The source characterization and optimal monitoring network design methodologies are used sequentially for a contaminated aquifer site located in New South Wales, Australia. Performance of the integrated optimal source characterization methodology combining linked simulation-optimization, fractal singularity mapping technique (FSMT) and Pareto optimal solutions is evaluated. This study presents an integrated application of optimal source characterization with spatiotemporal concentration measurement data obtained from sequentially designed monitoring networks. The proposed sequential source characterization and monitoring network design methodology shows efficiency in identifying the unknown source characteristics. The designed monitoring network achieves comparable efficiency and accuracy utilizing much smaller number of monitoring locations as compared to a more ideal scenario where concentration measurements from a very large number of widespread monitoring wells are available. The proposed methodology is potentially useful for efficient characterization of unknown contaminant sources in a complex contaminated aquifer site, where very little initial concentration measurement data are available. The illustrative application of the methodology to a real-life contaminated aquifer site demonstrates the capability and efficiency of the proposed methodology.

Theory-guided full convolutional neural network: An efficient surrogate model for inverse problems in subsurface contaminant transport

Identification of the location and strength of a contaminant source in an aquifer is a challenging but crucial task. Efficient surrogate models can be constructed to replace traditional time-consuming simulators while solving this inverse problem. In recent years, with the rapid development of machine learning (ML) algorithms, the artificial neural network (ANN) has been proven to be an efficient way for surrogate modeling. However, it may be difficult for ANN-based algorithms to learn the convection-dispersion equation and predict the contaminant concentration field due to their point-to-point learning scheme. Because of their strong localized features, the concentration fields can be seen as images. In contrast, the convolutional neural network (CNN) can extract spatial information better due to its convolutional structure. Herein, a theory-guided full convolutional neural network (TgFCNN) model is proposed to solve inverse problems in subsurface contaminant transport. TgFCNN can construct robust and reliable surrogate models with limited training realizations, and be further utilized for inverse modeling tasks. The loss function of TgFCNN comprises the residual of governing equations of contaminant transport, as well as data mismatch. Moreover, the iterative ensemble smoother (IES) method is employed to update the parameters while solving the inverse problems. The proposed TgFCNN model is evaluated in four scenarios. The results demonstrate that the TgFCNN model possesses strong generalization and extrapolation abilities, and satisfactory accuracy when estimating unknown contaminant source parameters, as well as the permeability field. The time consumption of the TgFCNN surrogate model for inverse tasks is also greatly reduced compared to using traditional simulators directly.

A Multidisciplinary Approach to Evaluate the Effectiveness of Natural Attenuation at a Contaminated Site

This study evaluates the natural attenuation of chlorinated hydrocarbons as remediation action in a contaminated site downtown the city of Parma (Italy). To achieve this goal, a combination of new investigation methods (bio-molecular analysis, compound specific isotope analysis, phytoscreening) has been proposed. The approach (named circular multi step) allows to: fully understand the phenomena that occur at the study site, design new investigation activities, and manage best practices. Consequently, each step of the approach improves the conceptual and numerical models with new knowledge. The activities carried out at the study site allowed to detect a contamination of perchloroethylene in a large part of the city of Parma and, of main importance, underneath a kindergarten. The results of the study did not show significant natural attenuation of chlorinated hydrocarbons and that the detected contamination could refer to the same unknown contaminant source. Furthermore, the innovative phytoscreening technique was applied to assess the presence of chlorinated hydrocarbons at the ground level. The plume spread was estimated through numerical modeling starting from potential contaminant sources. This study enhances the knowledge of groundwater flow and contamination in Parma and allows authorities to design new investigation/reclamation activities through management actions.

Source Characterization of Multiple Reactive Species at an Abandoned Mine Site Using a Groundwater Numerical Simulation Model and Optimization Models.

The most important first step in the management and remediation of contaminated groundwater aquifers is unknown contaminant source characterization. Often, the hydrogeological field data available for accurate source characterization are very sparse. In addition, hydrogeological and geochemical parameter estimates and field measurements are uncertain. Particularly in complex contaminated sites such as abandoned mine sites, the geochemical processes are very complex and identifying the sources of contamination in terms of location, magnitude, and duration, and determination of the pathways of pollution become very difficult. The reactive nature of the contaminant species makes the geochemical transport process very difficult to model and predict. Additionally, the source identification inverse problem is often non-unique and ill posed. This study is about developing and demonstrating a source characterization methodology for a complex contaminated aquifer with multiple reactive species. This study presents linked simulation optimization-based methodologies for characterization of unknown groundwater pollution source characteristics, i.e., location, magnitude and duration or timing. Optimization models are solved using an adaptive simulated annealing (ASA) optimization algorithm. The performance of the developed methodology is evaluated for different complex scenarios of groundwater pollution such as distributed mine waste dumps with reactive chemical species. The method is also applied to a real-life contaminated aquifer to demonstrate the potential applicability and optimal characterization results. The illustrative example site is a mine site in Northern Australia that is no longer active.

Solving Inverse Problems of Unknown Contaminant Source in Groundwater-River Integrated Systems Using a Surrogate Transport Model Based Optimization

The paper presents a new approach to identify the unknown characteristics (release history and location) of contaminant sources in groundwater, starting from a few concentration observations at monitoring points. An inverse method that combines the forward model and an optimization algorithm is presented. To speed up the computation, the transfer function theory is applied to create a surrogate transport forward model. The performance of the developed approach is evaluated on two case studies (literature and a new one) under different scenarios and measurement error conditions. The literature case study regards a heterogeneous confined aquifer, while the proposed case study was never investigated before, it involves an aquifer-river integrated flow and transport system. In this case, the groundwater contaminant originated from a damaged tank, migrates to a river through the aquifer. The approach, starting from few concentration observations monitored at a downstream river cross-section, accurately estimates the release history at a groundwater contaminant source, even in presence of noise on observations. Moreover, the results show that the methodology is very fast, and can solve the inverse problem in much less computation time in comparison with other existing approaches.

Identifying groundwater contaminant sources based on a KELM surrogate model together with four heuristic optimization algorithms

Identifying groundwater contaminant sources involves a reverse determination of the source characteristics by monitoring contaminant concentrations in a few observation wells. However, due to the ill-posed nature and high time consumption of identification, an efficient identification process with accurate estimated results is particularly important. To improve the efficiency of identifying groundwater contaminant sources, a kernel-based extreme learning machine was used as a surrogate for the time-consuming simulation model. Four heuristic search algorithms were used to improve the accuracy of the identification results. The proposed approach was tested in both hypothetical and actual cases. The conclusions are: 1. By forward and backward calculation of the surrogate model, the time cost of identifying groundwater sources can be reduced significantly; 2. When a traditional genetic algorithm and a particle swarm optimization algorithm are combined with quantum computing, computational efficiency and accuracy are both improved; and 3. By using various search algorithms to identify unknown contaminant sources in the actual case, the range of release histories of each contaminant source can be obtained, decreasing the ill-posed nature of the identification result obtained by a single algorithm and improving the reliability of the identification results.

Identification of transient contaminant sources in aquifers through a surrogate model based on a modified self-organizing-maps algorithm

The identification of transient groundwater contaminant sources in terms of source locations, contaminant magnitudes, and active durations remains a challenge. The problem becomes more complex due to spatial heterogeneity, sparse observation data, concentration measurement errors, and unexpected uncertainty. This study addresses this challenge by proposing a modified self-organizing maps (SOM) algorithm; this algorithm can improve the physically-based models by reducing the computational burden more efficiently. The method sufficiently increases the accuracy and efficiency for identifying the contaminant source, because the trained SOM-based surrogate models can identify the source characteristics independently without necessarily operating a formal linked simulation-optimization model. The performance of the proposed method was assessed on a hypothetical heterogeneous aquifer model; the assessment considered unknown observation data, concentration measurement errors, and an unknown pumping well. The proposed SOM-based surrogate model can not only approximate the results from the groundwater flow and transport simulation models, but it can also be used in lieu of the optimization model in a more efficient way for identifying the unknown transient contaminant sources in groundwater systems.

Identification of multiple contamination sources using variational continuous assimilation

A variational continuous assimilation (VCA) method was proposed as an estimation method for multiple unknown contaminant sources in outdoor conditions. The VCA method is a data assimilation method that minimizes the difference between calculated and observed concentrations by adding a correction term to the governing equation of the computational fluid dynamics (CFD) model. The method was validated via a set of numerical experiments, in which single and multiple unknown source locations and their emission rates were estimated using the VCA method with limited observation data. The results showed that the VCA method could be used to roughly estimate source locations and emission rates, and could reproduce the correct concentration fields. The estimated concentration fields are, however, slightly more widely distributed than the actual concentration fields and are distributed slightly windward of the actual source locations. The estimation accuracy in the case with two sources was equivalent to the case with a single source.

Application of a link simulation optimization model utilizing quantification of hydrogeologic uncertainty to characterize unknown groundwater contaminant sources

In existing groundwater contamination source characterization methodologies, simulation models estimate the contamination concentration in the study area. In order to obtain reliable solutions, it is essential to provide the simulation models with reliable hydrogeological properties. In real-life scenarios often high level of uncertainty and variability is associated with the hydrogeological properties. This study focuses on quantifying the hydrogeological parameter uncertainty to enhance the accuracy of identifying contamination release histories. Tracer experiment results at the Eastlakes Experimental Site, located in Botany Sands Aquifer, in New South Wales, Australia, are utilized to examine the performance and potential applicability of the methodology. In the selected study area, the hydrogeological heterogeneity in the microscopic scale, specifically the hydraulic conductivity, has substantial effect on the transport of pollutants. Among available tracer information, Bromide is studied as a conservative contaminant. Using possible realizations of the flow field, a coefficient of confidence (COC) is calculated for each field monitoring locations and times. Higher COC implies that the result of simulation models at that specific monitoring location and time is more reliable than other contaminant concentration data. Therefore, the optimization model should emphasise matching the corresponding estimated and observed contamination concentrations to accurately identify the contaminant release locations and histories. The linked simulation–optimisation method is utilised to optimally characterise the Bromide sources. Performance evaluation results demonstrate that the proposed methodology recovers pollution source characteristics more accurately compared to the methodology which does not consider the effect of hydrogeological parameter uncertainty.

Identification of Groundwater Contamination Sources Using Meshfree RPCM Simulation and Particle Swarm Optimization

In this study, a simulation-optimization (SO) model is presented by coupling a meshfree based simulator using radial point collocation method (RPCM) and particle swarm optimization (PSO) as optimizer to identify the unknown groundwater contaminant sources from the measured/simulated contaminant concentration data in the aquifer. To demonstrate the approach, two case studies have been presented. The first example is a hypothetical case which simulates the contaminant releases from several disposal sites in an aquifer during four years release period. The second case considered is a field study where leaching of contaminant, during their storage, from disposal sites at several locations in the aquifer leads to contamination of the groundwater. The goal in both cases was to reconstruct the contaminant release history from the disposal sites and their magnitudes from the given historical concentration data at a few observation wells in the aquifer. It was observed that the source identification model could reconstruct the release histories from the waste disposal sites in both the cases accurately. This study demonstrated that PSO based optimization model with a meshfree flow and transport simulator can be effectively used for groundwater contaminant source identification problems.

SELF-ORGANIZING MAP BASED SURROGATE MODELS FOR CONTAMINANT SOURCE IDENTIFICATION UNDER PARAMETER UNCERTAINTY

Identification of unknown groundwater contaminant sources is a complex problem. The complexities arise mainly due to the uncertainties related to the hydrogeologic information, sparsity of measurement data and unavoidable concentration measurement errors. The process of contaminant source identification with sparse and limited concentration measurement data especially when the hydrogeologic parameters are uncertain requires an efficient procedure. The existing methodologies to tackle this problem in real world cases usually require huge computational time and the solutions may be non-unique. The goal of this study is to evaluate a developed methodology to characterize the groundwater contamination sources in a heterogeneous, multi layered aquifer. This developed methodology utilizes the Self Organizing Maps (SOM) algorithm to design the surrogate models for source characterization. The most important advantages is that in this methodology, the trained SOM based surrogate models is directly utilized for groundwater contaminant source characterization without the necessity of using a separate linked simulation optimization model. The performance of the developed methodology is evaluated by using deterministic hydraulic conductivity values, and uncertain hydraulic conductivity values. These results indicate that the developed methodology could efficiently approximate groundwater flow and transport simulation models, and also characterize unknown groundwater contaminant sources in terms of location, magnitude and release history.

Indoor contaminant source identification by inverse zonal method: Levenberg–Marquardt and conjugate gradient methods

ABSTRACTIndoor contaminant characteristics have been identified traditionally by inverse computational fluid dynamics methods. However, these methods are too time-consuming and costly. In this study, an inverse zonal method was developed as a simple model to provide an alternative and inexpensive approach for identifying indoor contaminant source characteristics. In the direct problem, experimental data and computational fluid dynamics results were employed to validate the reliability of the direct zonal method. This comparison showed the capability of the direct zonal method to simulate the contaminant airflow distribution in indoor environments. Finally, the inverse zonal method was used to predict the location and strength of an unknown indoor contaminant source with prior knowledge of the source release time. The inverse techniques for solving the identification problem were the Levenberg−Marquardt and conjugate gradient methods. The results showed that these inverse methods could identify the characteristics of a gaseous emission source.Abbreviations: CFD: computational fluid dynamics; ICFD: inverse computational fluid dynamics; IZM: inverse zonal model; POMA: pressurized zonal model with the air diffuser; QR: quasi-reversibility

Evaluation of Unknown Groundwater Contaminant Sources Characterization Efficiency under Hydrogeologic Uncertainty in an Experimental Aquifer Site by Utilizing Surrogate Models

Characterization of unknown groundwater contaminant sources is an important but difficult step in effective groundwater management. The difficulties arise mainly due to the time of contaminant detection which usually happens a long time after the start of contaminant source(s) activities. Usually, limited information is available which also can be erroneous. This study utilizes Self-Organizing Map (SOM) and Gaussian Process Regression (GPR) algorithms to develop surrogate models that can approximate the complex flow and transport processes in a contaminated aquifer. The important feature of these developed surrogate models is that unlike the previous methods, they can be applied independently of any linked optimization model solution for characterizing of unknown groundwater contaminant sources. The performance of the developed surrogate models is evaluated for source characterization in an experimental contaminated aquifer site within the heterogeneous sand aquifer, located at the Botany Basin, New South Wales, Australia. In this study, the measured contaminant concentrations and hydraulic conductivity values are assumed to contain random errors. Simulated responses of the aquifer to randomly specified contamination stresses as simulated by using a three-dimensional numerical simulation model are utilized for initial training of the surrogate models. The performance evaluation results obtained by using different surrogate models are also compared. The evaluation results demonstrate the different capabilities of the developed surrogate models. These capabilities lead to development of an efficient methodology for source characterization based on utilizing the trained and tested surrogate models in an inverse mode. The obtained results are satisfactory and show the potential applicability of the SOM and GPR-based surrogate models for unknown groundwater contaminant source characterization in an inverse mode.

Adaptive Surrogate Model Based Optimization (ASMBO) for Unknown Groundwater Contaminant Source Characterizations Using Self-Organizing Maps

Characterization of unknown groundwater contaminant sources in terms of location, magnitude and duration of source activity is a complex problem. In this study, to increase the efficiency and accuracy of source characterization an alternative methodology to the methodologies proposed earlier is developed. This methodology, Adaptive Surrogate Modeling Based Optimization (ASMBO) uses the capabilities of Self Organizing Map (SOM) algorithm to design the surrogate models and adaptive surrogate models for source characterization. The most important advantage of this methodology is its direct utilization for groundwater contaminant characterization without the necessity of utilizing a linked simulation optimization model. The validation of the SOM based surrogate models and SOM based adaptive surrogate models demonstrates that the quantity and quality of initial sample sizes have crucial role on the accuracy of solutions as the designed monitoring locations. The performance evaluation results of the proposed methodology are obtained using error free and erroneous concentration measurement data. These results demonstrate that the developed methodology could approximate groundwater flow and transport simulation models, and substitute the optimization model for characterization of unknown groundwater contaminant sources in terms of location, magnitude and duration of source activity.

Linking a Simulated Annealing Based Optimization Model with PHT3D Simulation Model for Chemically Reactive Transport Processes to Optimally Characterize Unknown Contaminant Sources in a Former Mine Site in Australia

Historical mining activities often lead to continuing wide spread contaminants in both groundwater and surface water in previously operational mine site areas. The contamination may continue for many years after closing down the mining activities. The essential first step for sustainable management of groundwater and development of remediation strategies is the unknown contaminant source characterization. In a mining site, there are multiple species of contaminants involving complex geochemical processes. It is difficult to identify the potential sources and pathways incorporating the chemically reactive multiple species of contaminants making the source characterization process more challenging. To address this issue, a reactive transport simulation model PHT3D is linked to a Simulated Annealing based the optimum decision model. The numerical simulation model PHT3D is utilized for numerically simulating the reactive transport process involving multiple species in the former mine site area. The simulation results from the calibrated PHT3D model are illustrated, with and without incorporating the chemical reactions. These comparisons show the utility of using a reactive, geochemical transport process’ simulation model. Performance evaluation of the linked simulation optimization methodology is evaluated for a contamination scenario in a former mine site in Queensland, Australia. These performance evaluation results illustrate the applicability of linked simulation optimization model to identify the source characteristics while using PHT3D as a numerical reactive chemical species’ transport simulation model for the hydro-geochemically complex aquifer study area.