Simulation Of Groundwater Contaminant Transport Research Articles

Numerical simulation of groundwater contaminant transport in porous media

Groundwater has been considered as a major pathway for contamination migration which results in both subsurface pollution and soil contamination. This investigation makes an attempt to set up a two-dimensional numerical model to simulate groundwater flow and pollutant transport through porous media. The numerical model is used to simulate both groundwater flow and contaminant migration in porous media; besides, it is capable of considering the effects of sorption, retardation, and volatilization on the contaminant. Jajrud rural district located near Tehran City in Iran is a potentially contaminated site; furthermore, it is regarded as a potential peril for that zone. Groundwater flow and contaminant transport in Jajrud rural been investigated. This model is based on the finite-difference method which has enough ability to predict the pollution migration, and it is validated by several analytical and experimental test cases. The result revealed that the advection could possibly have been regarded as the main process on the contaminant transport in both reactive and non-reactive form of pollutant, as a result of high velocity in this region; furthermore, the reactive pollutant concentration mitigates 1.33 times more that non-reactive one as a result of high dispersion coefficient and contaminant decay term.

Numerical simulation of groundwater contaminant transport in unsaturated flow

Abstract A sand-box experiment equipment is constructed to simulate the transportation of contaminant in unsaturated flow under dirichlet boundary condition. Based on analysis of the mechanism in unsaturated migration, taking a riverland as an example, the model of water quality which was affected by contaminant transport was built, and the calculation values coincided well with experimental data. The results showed that the lateral migration should not be neglected when seepage velocity was high. In addition, the lateral migration was faster in high moisture content areas and the solute was retained for a short time; however, the situation was reversed in low moisture content areas. Moreover, during the initial stage of solute transport, the velocity of the trailing end of the pinnate pollution zone was high under the effect of capillary action, which would lead to expansion of the range of pollution. Furthermore, the relative concentration curve of pollutants presented a single peak state, with the peak value of the curve increasing with the velocity, and the arrival time of the peak value was also gradually advanced. Therefore, this study can provide reference for the prediction and control of groundwater contaminant transport in an unsaturated zone.

Assessment of artificial neural network models based on the simulation of groundwater contaminant transport

The characterization of contaminant transport in subsurface environments is a pre-requisite for sustainable groundwater use and management. Various analytical and numerical models are generally utilized for such characterization. Analytical models are used for solving simple and idealized pollution transport problems, while numerical models deal with real-world pollution transport simulations. However, in situations where any of the aquifer parameters (such as geological properties, boundary conditions, initial conditions, etc.) are not explicitly defined, use of these models becomes redundant. Simulation of pollution transport under such scenarios becomes challenging. To tackle such problems, researchers generally make use of artificial neural network (ANN) models. Existing literature review reveals that feed-forward backpropagation (FFBNN) models are the most commonly used training method while the applicability of other ANN models has not been appropriately explored. In this study, various ANN models, encompassing supervised and unsupervised neural networks, like, cascade-forward backpropagation (CFBNN), FFBNN, radial basis function (RBFNN), exact radial basis function (ERBFNN) and generalized regression (GRNN), have been developed, and their performances are compared for transport simulation of a conservative pollutant in a two-dimensional hypothetical aquifer. The models reported have pollution injection rates and locations of the sources as input data and the pollution concentrations measured in some water supply wells (or monitoring wells) are considered as target data. Stability of the developed models has been validated by sensitivity analyses. This study reveals that alternative ANN models (other than the ones already reported in literature) can be reliable for simulation of pollutant transport in groundwater systems.

Simulation of Groundwater Contaminant Transport at a Decommissioned Landfill Site—A Case Study, Tainan City, Taiwan

Contaminant transport in subsurface water is the major pathway for contamination spread from contaminated sites to groundwater supplies, to remediate a contaminated site. The aim of this paper was to set up the groundwater contaminant transport model for the Wang-Tien landfill site, in southwestern Taiwan, which exhibits high contamination of soil and groundwater and therefore represents a potential threat for the adjacent Hsu-Hsian Creek. Groundwater Modeling System software, which is the most sophisticated groundwater modeling tool available today, was used to numerically model groundwater flow and contaminant transport. In the simulation, the total mass of pollutants in the aquifer increased by an average of 72% (65% for ammonium nitrogen and 79% for chloride) after 10 years. The simulation produced a plume of contaminated groundwater that extends 80 m in length and 20 m in depth northeastward from the landfill site. Although the results show that the concentrations of ammonium nitrogen and chlorides in most parts are low, they are 3.84 and 467 mg/L, respectively, in the adjacent Hsu-Hsian Creek.

2D groundwater contaminant transport modeling by using the finite volume method on an unstructured grid system

This paper introduces a finite volume unstructured algorithm for groundwater contaminant simulation. The algorithm is developed using the direct finite volume concept constructed on an unstructured grid system. The cell-centered explicit conservative scheme has been adopted in numerical simulation. An example of groundwater contaminant transport simulation on a simple geometry is presented and results are compared with an analytic solution and the solutions obtained by using a finite difference method on structured grids. The direct finite volume method applied on an arbitrary unstructured grid system is found promising for modeling groundwater contamination transport behavior.

Monitoring network design to provide initial detection of groundwater contamination

The design of a monitoring network to provide initial detection of groundwater contamination at a waste disposal facility is complicated by uncertainty in both the characterization of the subsurface and the nature of the contaminant source. In addition, monitoring network design requires the resolution of multiple conflicting objectives. A method is presented that incorporates system uncertainty in monitoring network design and provides network alternatives that are noninferior with respect to several objectives. Monte Carlo simulation of groundwater contaminant transport is the method of uncertainty analysis. The random inputs to the simulation are the hydraulic conductivity field and the contaminant source location. The design objectives considered are (1) minimize the number of monitoring wells, (2) maximize the probability of detecting a contaminant leak, and (3) minimize the expected area of contamination at the time of detection. The network design problem is formulated as a multiobjective, integer programming problem and is solved using simulated annealing. An application of the method illustrates the configurations of noninferior network solutions and the trade‐offs between objectives. The probability of detection can be increased either by using more monitoring wells or by locating the wells farther from the source. The latter case results in an increase in the average area of the detected contaminant plumes at the time of initial detection. If monitoring is carried out very close to the contaminant source to reduce the expected area of a detected plume, a large number of wells are required to provide a high probability of detection. A sensitivity analysis showed that the predicted performance of a given number of wells decreases significantly as the heterogeneity of the porous medium increases. In addition, a poor estimate of hydraulic conductivity was shown to result in optimistic estimates of network performance. In general, the trade‐offs between monitoring objectives are an important factor in network design unless the cost (as expressed by the number of monitoring wells) is of limited concern.

Large scale nonlinear deterministic and stochastic optimization: Formulations involving simulation of subsurface contamination

Once subsurface water supplies become contaminated, designing cost-effective and reliable remediation schemes becomes a difficult task. The combination of finite element simulation of groundwater contaminant transport with nonlinear optimization is one approach to determine the best well selection and optimal fluid withdrawal and injection rates to contain and remove the contaminated water. Both deterministic and stochastic programming problems have been formulated and solved. These tend to be large scale problems, owing to the simulation component which serves as a portion of the constraint set. The overall problem of combined groundwater process simulation and nonlinear optimization is discussed along with example problems. Because the contaminant transport simulation models give highly uncertain results, quantifying their uncertainty and incorporating reliability into the remediation design results in a class of large stochastic nonlinear problems. The reliability problem is beginning to be addressed, and some strategies and formulations involving chance constraints and Monte Carlo methods are presented.