Unknown Groundwater Pollution Sources Research Articles

CHARACTERIZATION OF GROUNDWATER POLLUTION SOURCES BY KRIGING BASED LINKED SIMULATION OPTIMIZATION

Often untreated wastes are buried underground as convenient method of disposal. These clandestine disposal methods are adopted to offset the cost incurred in proper waste management. Though there might be short term economic gain to the disposer but the long-term impact of such disposal manifests in form of groundwater pollution, which if left unchecked would potentially pollute the entire aquifer. Any contaminated aquifer treatment technique requires prior information about the pollutant sources characteristics. Thus, characterization of unknown groundwater pollution sources becomes imperative in remediation process. The first step involves the identification of number of sources and their locations. In a clandestine scenario the entire study area is treated as a potential source and the search for the actual source can be extensively rigorous in space and time. To overcome this challenge a kriging based zoning is adopted to narrow down the search space for the actual sources in a limited data availability scenario. A comparative analysis of two methods is presented; first method where only (Simulated Annealing based Linked Simulation Optimization) SALSO is used, and second method where kriging based zoning plus SALSO is adopted for pollution source characterization. It is found that kriging method clubbed with SALSO gives significantly better results compared to initial results. A boundary of Indian Institute of Technology Patna with hypothetical hydro-geological parameters is used to test the developed method. The location of the pollutant sources is easily identified when a kriging based Simulated Annealing Linked Simulation Optimization (KSALSO) method is adopted.

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.

A least squares method for identification of unknown groundwater pollution source

Abstract The identification of unknown groundwater pollution sources is one of the most important premises in groundwater pollution prevention and remediation. In this paper, an exploratory application of a least squares method to identify the unknown groundwater pollution source is conducted. Supported by a small amount of observation data and the analytical solutions of the pollutant transport model, the initial concentration, the leakage location and the pollutant mass are identified by using the least squares method under a sand tank experiment and a gas station area. In the sand tank experiment, it is found that the fitting errors of three cross-sections are within 6%. In the gas station area, it is found that the results are nearly consistent with the site investigation information. The results indicate that the least squares method has considerable application values in the identification of groundwater pollution sources.

Identification of multiple unknown point sources occurring in the 2D transport equation: application to groundwater pollution source identification

Accessing quality water is of crucial importance to both society and the environment. Deterioration in water quality through groundwater pollution presents a substantial risk to human health, plant and animal life, and detrimental effects on the local economy. To ensure groundwater quality, there is need to identify locations of unknown groundwater pollution sources. In this paper, the locations of groundwater contaminant sources have been identified using inverse problem technique. The work in this paper concerns the inverse source problem in the Advection Dispersion Reaction Equation (ADRE) with an emphasis on groundwater pollution source identification. Mathematically, inverse source problem involves the reconstruction of the source function in the ADRE from the boundary and interior measurements. An inverse source problem technique for identifying the unknown groundwater pollution source utilizing only the boundary and interior measurements is developed. The finite volume discretization method is employed on the adjoint ADRE to provide the data. The data from the finite volume method results into Volterra integral equation which after discretizing transforms into an ill-posed inverse problem. Tikhonov regularization method is used to achieve stability on the ill-posed problem. The results indicates that our proposed inverse problem is accurate with the data.

New Hybrid Optimization Methodology to Identify Pollution Sources Considering the Source Locations and Source Flux as Unknown

AbstractA hybrid methodology was developed for identifying unknown groundwater pollution sources. The source identification problem can be solved by using the inverse optimization approach. Being a...

An Effective Kalman Filter-Based Method for Groundwater Pollution Source Identification and Plume Morphology Characterization

The identification of unknown groundwater pollution sources and the characterization of pollution plume remains a challenging problem. In this study, we addressed this problem by a linked simulation-optimization approach. This approach couples a contaminant transport simulation model with a Kalman filter-based method to identify groundwater pollution source and characterize plume morphology. In the proposed methodology, the concentration field library, the covariance reduction with a Kalman filter, an alpha-cut technique of fuzzy set, and a linear programming model are integrated for solving this inverse problem. The performance of this methodology is evaluated on an illustrative groundwater pollution source identification problem. The evaluation considered the random hydraulic conductivity filed, erroneous monitoring data, a prior information shortage of potential pollution sources, and an unexpected and unknown pumping well. The identified results indicate that, under these conditions, the proposed Kalman filter-based optimization model can give satisfactory estimations to pollution sources and plume morphology for domains with small and moderate heterogeneity but cannot validate the transport in the relatively high heterogeneous field.

Identification of Groundwater Pollution Sources by a SCE-UA Algorithm-Based Simulation/Optimization Model

Prevention and remediation strategies for groundwater pollution can be successfully carried out if the location, concentration, and release history of contaminants can be accurately identified. This, however, presents a challenge due to complex groundwater systems. To address this issue, a simulation-optimization (S/O) model by integrating MODFLOW and MT3DMS into a shuffled complex evolution (SCE-UA) optimization algorithm was proposed; this coupled model can identify the unknown groundwater pollution source characteristics. Moreover, the Grids Traversal algorithm was used for automatically searching all possible combinations of pollution source location. The performance of the proposed S/O model was tested by three hypothetical scenarios with various combinations of mixed situations (i.e., single and multiple pollution source locations, known and unknown pollution source locations, steady-state flow and transient flow). The field measurement errors was additionally considered and analyzed. Our results showed that this proposed S/O model performed reasonably well. The identified locations and concentrations of contaminants fairly matched with the imposed inputs with average normalized deviations less than 1% after sufficient generations. We further assessed the impact of generation number on the performance of the S/O model. The performance could be significantly improved by increasing generation number, which yet resulted in a heavy computational burden. Furthermore, the proposed S/O model performed more efficiently and robustly than the traditionally used artificial neural network (ANN)-based model. This is due to the internal linkage of numerical simulation in the S/O model that promotes the data exchange from external files to programming variables. This new model allows for solving the source-identification problems considering complex conditions, and thus for providing a platform for groundwater pollution prevention and management.

Linked Optimal Reactive Contaminant Source Characterization in Contaminated Mine Sites: Case Study

AbstractApplication of a developed methodology for characterization of distributed contaminant sources in a contaminated mine site aquifer involving complex geochemical processes is presented. Linked simulation-optimization models are widely used as efficient tools for identifying unknown groundwater pollution sources. This is an essential first step in determining effective and reliable groundwater management and remediation strategies for a polluted aquifer. However, linking robust numerical models to simulate the transport processes of reactive chemical contaminant species in aquifers involving complex and highly nonlinear physical and geochemical process increases the computational burden extensively, and may affect the feasibility and efficiency of the methodology. To overcome this computational limitation and improve the computational feasibility by avoiding the necessity of repeated numerical simulations, genetic programming-based trained surrogate models are developed to approximately simulate suc...

Identification of Unknown Groundwater Pollution Sources and Determination of Optimal Well Locations Using ANN-GA Based Simulation-Optimization Model

The linked simulation-optimization model can be used for solving a complex groundwater pollution source identification problem. Advanced simulators have been developed and successfully linked with numerous optimization algorithms for identification of groundwater pollution sources. However, the identification of pollution sources in a groundwater aquifer using linked simulation-optimization model has proven to be computationally expensive. To overcome this computational burden, an approximate simulator, the artificial neural network (ANN) model can be used as a surrogate model to replace the complex time-consuming numerical simulation model. However, for large-scale aquifer system, the performance of the ANN-based surrogate model is not satisfactory when a single ANN model is used to predict the concentration at different observation locations. In such a situation, the model efficiency can be enhanced by developing separate ANN model for each of the observation locations. The number of ANN models is equal to the number of observation wells in the aquifer. As a result, the complexity of the ANN-based simulation-optimization model will be related to the number of observation wells. Thus, this study used a modified formulation to find out the optimal numbers of observation wells which will eventually reduce the computational time of the model. The performance of the ANN-based simulation-optimization model is evaluated by identifying the groundwater pollutant sources of a hypothetical study area. The limited evaluation shows that the model has the potential for field application.

Application of Unknown Groundwater Pollution Source Release History Estimation Methodology to Distributed Sources Incorporating Surface-Groundwater Interactions

Sources of contamination of groundwater are often difficult to characterize. However, it is essential for effective remediation of polluted groundwater resources. This study demonstrates an application of the linked simulation-optimization based methodology to estimate the release history from spatially distributed sources of pollution at an illustrative abandoned mine-site. In linked simulation-optimization approaches a numerical groundwater flow and transport simulation model is linked to the optimization model. In this study, topographic and geologic characteristics of the abandoned mine-site were simulated using a three-dimensional (3D) numerical groundwater flow model. Transport of contaminant in the groundwater was simulated using a 3D transient advective-dispersive contaminant transport model. Adsorption or chemical reaction of the contaminant was not considered in the contaminant transport model. Adaptive simulated annealing (ASA) was employed for solving the optimization problem. An optimization algorithm generates the candidate solutions corresponding to various unknown groundwater source characteristics. The candidate solutions are used as input in the numerical groundwater transport simulation model to generate the concentration of pollutant in the study area. This information is used to calculate the objective function value, which is utilized by the optimization algorithm to improve the candidate solution. This process continues until an optimal solution is obtained. Optimal solutions obtained in this study show that the linked simulation-optimization based methodology is potentially applicable for the characterization of spatially distributed pollutant sources, typically present at abandoned mine-sites.

Development of Unknown Pollution Source Identification Models Using GMS ANN–Based Simulation Optimization Methodology

AbstractThe detection of groundwater pollution sources is an important but a very difficult task. The location and magnitude of groundwater pollution sources can be identified using inverse optimization technique. The technique is also known as simulation-optimization approach where the aquifer simulation model is incorporated with the optimization model for finding the unknown pollution sources in an aquifer. The efficiency of the simulation-optimization model is highly related to the performance of the simulation model. This study develops three improved methodologies for identification of unknown groundwater pollution sources. In the first approach, the groundwater modeling system (GMS) is linked with the optimization model for solving source identification problem. The optimization model is solved using the direct-search method. The incorporation of GMS with the optimization model allows for the solving of a bigger real-world pollution source identification problem. The challenge of this approach is t...

Optimal Unknown Pollution Source Characterization in a Contaminated Groundwater Aquifer—Evaluation of a Developed Dedicated Software Tool

Precise identification of the pollutant source characteristics is the first step for designing an effective groundwater contamination remediation strategy. In this study a linked simulation-optimization based methodology is utilized for identification of unknown groundwater pollution sources in a real life contaminated aquifer in New South Wales, Australia where the source locations and source flux release history are the explicit unknown variables. The methodology is applied utilizing an in house software package GWSID developed at James Cook University for optimal determination of the unknown source characteristics. The methodology incorporates linked simulation optimization approach and utilizes simulated Algorithm as an evolutionary optimization algorithm. The performance evaluation results show practical utility of the methodology and of the associated developed computers software in identifying the unknown source characteristics.

Encapsulating the Role of Solution Response Space Roughness on Global Optimal Solution: Application in Identification of Unknown Groundwater Pollution Sources

A major challenge of any optimization problem is to find the global optimum solution. In a multi-dimensional solution space which is highly non-linear, often the optimization algorithm gets trapped around some local optima. Optimal Identification of unknown groundwater pollution sources poses similar challenges. Optimization based methodology is often applied to identify the unknown source characteristics such as location and flux release history over time, in a polluted aquifer. Optimization based models for identification of these characteristics of unknown ground-water pollution sources rely on comparing the simulated effects of candidate solutions to the observed effects in terms of pollutant concentration at specified sparse spatiotemporal locations. The optimization model minimizes the difference between the observed pollutant concentration measurements and simulated pollutant concentration measurements. This essentially constitutes the objective function of the optimization model. However, the mathematical formulation of the objective function can significantly affect the accuracy of the results by altering the response contour of the solution space. In this study, two separate mathematical formulations of the objective function are compared for accuracy, by incorporating different scenarios of unknown groundwater pollution source identification problem. Simulated Annealing (SA) is used as the solution algorithm for the optimization model. Different mathematical formulations of the objective function for minimizing the difference between the observed and simulated pollutant concentration measurements show different levels of accuracy in source identification results. These evaluation results demonstrate the impact of objective function formulation on the optimal identification, and provide a basis for choosing an appropriate mathematical formulation for unknown pollution source identification in contaminated aquifers.

Characterization of Groundwater Pollution Sources with Unknown Release Time History

Characterizations of unknown groundwater pollution sources in terms of source location, source flux release history and sources activity initiation times, from sparse observation concentration measurements are a challenging task. Optimization-based methods are often applied to solve groundwater pollution source characterization problem. These methods are effective only when the starting times of activity of the sources are precisely known, or the possible time window within which the sources activity actually start is known with a fair degree of certainty. However, in real life scenarios, the starting time of the activity of the sources is either unknown or can lie anywhere within a time window of years or decades. Absence of any prior information about the span of time window, within which the sources become active, makes existing source identification methodologies inefficient. As an alternative, an optimization-based source identification model is proposed, to simultaneously estimate source flux release history and sources activity initiation times. The method considers source flux release history and sources activity initiation times as explicit decision variables, optimally estimated by the decision model. Performance of the developed methodology is evaluated for an illustrative study area having multiple sources with different source activity initiation times, missing observation data and transient flow conditions. These evaluation results demonstrate the potential applicability of the proposed methodology and its capability to correctly estimate the unknown source flux releasing history and sources activity initiation times.

Multiobjective Monitoring Network Design for Efficient Identification of Unknown Groundwater Pollution Sources Incorporating Genetic Programming–Based Monitoring

In this study, a multiobjective model for optimal monitoring network design is presented, which aims to improve the accuracy of groundwater pollution source identification using concentration measurement from a designed optimal monitoring network. The proposed methodology combines the capability of genetic programming (GP) and linked simulation-optimization for recreating the flux history of the unknown conservative pollutant sources with limited number of spatiotemporal pollution concentration measurements. A selected subset of trained GP models is used to compute the impact factor of pollutant source fluxes at candidate monitoring locations. This impact factor is used as design criteria to find the best monitoring locations. While constraining the maximum number of permissible monitoring locations, the designed monitoring network improves the results of source identification by choosing monitoring locations that reduce the possibility of missing a pollution source. At the same time, the designed monitoring network decreases the degree of nonuniqueness in the set of possible aquifer responses to subjected geochemical stresses. The potential application of the developed methodology is demonstrated by evaluating its performance for an illustrative study area. These performance evaluation results show the improved efficiency in source identification when concentration measurements from the designed monitoring network are used.

Efficient Identification of Unknown Groundwater Pollution Sources Using Linked Simulation-Optimization Incorporating Monitoring Location Impact Factor and Frequency Factor

This study aims to improve the accuracy of groundwater pollution source identification using concentration measurements from a heuristically designed optimal monitoring network. The designed network is constrained by the maximum number of permissible monitoring locations. The designed monitoring network improves the results of source identification by choosing monitoring locations that reduces the possibility of missing a pollution source, at the same time decreasing the degree of non uniqueness in the set of possible aquifer responses to subjected geo-chemical stresses. The proposed methodology combines the capability of Genetic Programming (GP), and linked simulation-optimization for recreating the flux history of the unknown conservative pollutant sources with limited number of spatiotemporal pollution concentration measurements. The GP models are trained using large number of simulated realizations of the pollutant plumes for varying input flux scenarios. A selected subset of GP models are used to compute the impact factor and frequency factor of pollutant source fluxes, at candidate monitoring locations, which in turn is used to find the best monitoring locations. The potential application of the developed methodology is demonstrated by evaluating its performance for an illustrative study area. These performance evaluation results show the efficiency in source identification when concentration measurements from the designed monitoring network are utilized.

Sequential optimal monitoring network design and iterative spatial estimation of pollutant concentration for identification of unknown groundwater pollution source locations

One of the difficulties in accurate characterization of unknown groundwater pollution sources is the uncertainty regarding the number and the location of such sources. Only when the number of source locations is estimated with some degree of certainty that the characterization of the sources in terms of location, magnitude, and activity duration can be meaningful. A fairly good knowledge of source locations can substantially decrease the degree of nonuniqueness in the set of possible aquifer responses to subjected geochemical stresses. A methodology is developed to use a sequence of dedicated monitoring network design and implementation and to screen and identify the possible source locations. The proposed methodology utilizes a combination of spatial interpolation of concentration measurements and simulated annealing as optimization algorithm for optimal design of the monitoring network. These monitoring networks are to be designed and implemented sequentially. The sequential design is based on iterative pollutant concentration measurement information from the sequentially designed monitoring networks. The optimal monitoring network design utilizes concentration gradient information from the monitoring network at previous iteration to define the objective function. The capability of the feedback information based iterative methodology is shown to be effective in estimating the source locations when no such information is initially available. This unknown pollution source locations identification methodology should be very useful as a screening model for subsequent accurate estimation of the unknown pollution sources in terms of location, magnitude, and activity duration.

Optimisation approach for pollution source identification in groundwater: an overview

Groundwater pollution occurs from different anthropogenic sources like leakage from Underground Storage Tanks (USTs) and depositories, leakage from hazardous waste dump sites and soak pits. Remediation of these contaminated sites requires optimal decision-making system so that the remediation is done in a cost-effective and efficient manner. Identification of unknown pollution sources plays an important role in remediation and containment of contaminant plume in a hazardous site. This paper reviews different optimisation algorithms like classical, nonclassical such as Genetic Algorithm, Artificial Neural Network and Simulated Annealing and hybrid methods, which can be applied for optimal identification of unknown groundwater pollution sources.

Identification of unknown groundwater pollution sources using classical optimization with linked simulation

Identification of unknown groundwater pollution sources still remains a challenging problem. Some of the complexities include: sparse observation data, substantial variation in magnitude of the source fluxes distributed over space and time, uncertainties in the imposed initial and boundary conditions. Methodologies already developed for optimal identification of pollution sources using concentration measurements and hydraulic data suffer from a number of limitations. As an alternative, a source identification methodology is proposed that uses a classical nonlinear optimization model linked to a flow and transport simulation model. The groundwater flow and transport simulator is linked to the nonlinear optimization model as an external module. The essential link between the simulator and the optimization method are the derivatives or gradient information required for the optimization algorithm. This proposed methodology is potentially applicable to large scale study areas and does not posses some of the computational limitations of some earlier developed methodologies, using nonlinear programming with the flow and transport process governing equations embedded as equality constraints within the optimization model. Performance of the proposed source identification methodology using spatiotemporal pollutant concentration measurements are evaluated by solving illustrative problems. Two different optimization formulations models are developed. The relative importance of the model formulations is demonstrated in terms of computational efficiency. The limited performance evaluations reported here demonstrate the potential applicability of the developed methodology using nonlinear programming and linked flow and transport simulation model for a fairly large study area with multiple unknown pollution sources.

A linked simulation–optimization model for solving the unknown groundwater pollution source identification problems

This study proposes a linked simulation–optimization model for solving the unknown groundwater pollution source identification problems. In the proposed model, MODFLOW and MT3DMS packages are used to simulate the flow and transport processes in the groundwater system. These models are then integrated with an optimization model which is based on the heuristic harmony search (HS) algorithm. In the proposed simulation–optimization model, the locations and release histories of the pollution sources are treated as the explicit decision variables and determined through the optimization model. Also, an implicit solution procedure is proposed to determine the optimum number of pollution sources which is an advantage of this model. The performance of the proposed model is evaluated on two hypothetical examples for simple and complex aquifer geometries, measurement error conditions, and different HS solution parameter sets. Identified results indicated that the proposed simulation–optimization model is an effective way and may be used to solve the inverse pollution source identification problems.