Contaminant Transport Simulation Model Research Articles

Deterministic Methodology for Determining the Optimal Sampling Frequency of Water Quality Monitoring Systems

This paper proposes a novel deterministic methodology for estimating the optimal sampling frequency (SF) of water quality monitoring systems. The proposed methodology is based on employing two-dimensional contaminant transport simulation models to determine the minimum SF, taking into consideration all the potential changes in the boundary conditions of a water body. Two-dimensional contaminant transport simulation models (RMA4) were implemented to estimate the distribution patterns of some effective physiochemical parameters within the Al-Hammar Marsh in the southern part of Iraq for 30 cases of potential boundary conditions. Using geographical information system (GIS) tools, a spatiotemporal analysis approach was applied to the results of the RMA4 models to determine the minimum SF of the monitoring stations with a monitoring accuracy (MA) level of detectable change in contaminant concentration ranging from the standard level to 50% (stepwise 5%). For the study area, the proposed methodology specified a minimum and maximum SF for each monitoring station (MS) that ranged between 12 and 33 times per year, respectively. An exponential relationship between SF and MA was obtained. This relationship shows that increasing the MA to ±10%, ±25%, and ±50% increases the SF by approximately 14%, 28%, and 93%, respectively. However, the proposed methodology includes all the potential values and cases of flow and contaminant transport boundary conditions, which increases the certainty of monitoring the system and the efficiency of the SF schedule. Moreover, the proposed methodology can be effectively applied to all types of surface water resources.

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.

Reliability Evaluation of Groundwater Contamination Source Characterization under Uncertain Flow Field

Groundwater contamination is one of the serious environmental problems. Effective remediation strategies require accurate characteristics of contamination sources. Contamination source identification approaches need accurate flow and contaminant transport simulation models. In order to obtain reliable solutions, the simulation models need to be provided with reliable hydrogeologic information. In real life scenarios usually sparse and limited hydrogeologic information is available. In this study two hydraulic conductivity sampling networks are ranked based on their effectiveness in identifying reliable contamination source characteristics. Using multiple realizations of hydraulic conductivity fields, and the location and size of the contaminant plume at different monitoring stages, an index of reliability is estimated for each hydraulic conductivity sampling network. It is demonstrated that the source characteristics identified by utilizing the sampling network with higher index of reliability results in more accurate characterization of contamination sources. Therefore the developed methodology provides a tool to select an appropriate hydrogeologic sampling network for more efficient characterizing of contamination sources.

An almost-parameter-free harmony search algorithm for groundwater pollution source identification

The spatiotemporal characterization of unknown sources of groundwater pollution is frequently encountered in environmental problems. This study adopts a simulation-optimization approach that combines a contaminant transport simulation model with a heuristic harmony search algorithm to identify unknown pollution sources. In the proposed methodology, an almost-parameter-free harmony search algorithm is developed. The performance of this methodology is evaluated on an illustrative groundwater pollution source identification problem, and the identified results indicate that the proposed almost-parameter-free harmony search algorithm-based optimization model can give satisfactory estimations, even when the irregular geometry, erroneous monitoring data, and prior information shortage of potential locations are considered.

Impacts of Physical and Chemical Heterogeneities on Aquifer Remediation Design

The impacts of physical and chemical aquifer heterogeneities on optimal remediation design, costs, and time to compliance are investigated by linking a genetic algorithm with a contaminant transport simulation model. Physical and chemical aquifer heterogeneities were grouped into three levels as follows: (1) hydraulic conductivity (K) heterogeneity only; (2) combined heterogeneity of K and the distribution coefficient (Kd); and (3) combined heterogeneity of K, Kd, and the mass transfer rate (α). Various degrees of heterogeneity were considered, ranging from slightly heterogeneous to strongly heterogeneous. Impacts were evaluated using two different optimization models: the optimal design model and the time-to-compliance model. The first model focused on finding optimal aquifer remediation designs and costs under various heterogeneity conditions, and the second model optimized the time needed to meet the water quality goals for a fixed pumping schedule. Results show that the variability in the remediation ...

Quantitative Estimates of the Uncertainty in the Evaluation of Ground Water Remediation Schemes

Due to the natural heterogeneity of subsurface formations and to the often limited number of data, the definition of the physical and chemical parameters that govern flow and contaminant transport processes is normally characterized by a high level of uncertainty. Uncertainty in the relevant input parameters leads to uncertainty in modeling predictions, which may influence the selection of the optimal remedial alternative. This paper describes a statistical procedure for quantifying the uncertainty in modeling predictions made in support of the evaluation of ground water remediation designs. The hydraulic conductivity field is defined as a random spatial variable whose statistical structure is inferred from the available hydraulic conductivity data. Monte Carlo simulations are used to generate equiprobable multiple realizations of the hydraulic conductivity conditioned on the available hydraulic conductivity measurements. Using Bayes’theorem, probability-based weights for each of the hydraulic conductivity realizations are estimated from “soft” data consisting of historical concentration data and contamination source information. The probability weights are expressed in terms of the simulated concentration distributions and the observed concentration data. Ground water flow and contaminant transport simulation models are then used to probabilistically evaluate and optimize ground water remediation schemes based on the statistical analysis of key model predictions, such as the probability of a particular part of the aquifer being within the capture zone of an extraction well, or the recovered mass of contaminants as a function of time.

On uncertainty in remediation analysis: variance propagation from subsurface transport to exposure modeling

Addressing long-term potential human exposures to, and health risks from contaminants in the subsurface environment requires the use of models. Because these models must project contaminant behavior into the future, and make use of highly variable landscape properties, there is uncertainty associated with predictions of long-term exposure. Many parameters used in both subsurface contaminant transport simulation and health risk assessment have variance owing to uncertainty and/or variability. These parameters are best represented by ranges or probability distributions rather than single values. Based on a case study with information from an actual site contaminated with trichloroethylene (TCE), we demonstrate the propagation of variance in the simulation of risk using a complex subsurface contaminant transport simulation model integrated with a multi-pathway human health risk model. Ranges of subsurface contaminant concentrations are calculated with the subsurface transport simulator T2VOC (using the associated code ITOUGH2 for uncertainty analysis) for a three-dimensional system in which TCE migrates in both the vadose and saturated zones over extended distances and time scales. The subsurface TCE concentration distributions are passed to CalTOX, a multimedia, multi-pathway exposure model, which is used to calculate risk through multiple exposure pathways based on inhalation, ingestion and dermal contact. Monte Carlo and linear methods are used for the propagation of uncertainty owing to parameter variance. We demonstrate how rank correlation can be used to evaluate contributions to overall uncertainty from each model system. In this sample TCE case study, we find that although exposure model uncertainties are significant, subsurface transport uncertainties are dominant.

A sensitivity analysis and parametric study for the evaluation of the optimal management of a contaminated aquifer

A typical groundwater remedation problem is studied by using a combined simulation-optimization model. The management procedure employs groundwater flow and contaminant transport simulation models in conjunction with linear and quadratic programming techniques. The methodology is applied to the hydrodynamic control of a contaminant plume that has to be stabilized and removed by a system of pumping wells. The paper focuses mainly upon a sensitivity analysis to the aquifer transmissivity. The effect of changes in the transmissivities of a zoned aquifer upon the optimal solutions of the management problem is examined by considering the optimal pumping rates, the time to remediation and the pumped groundwater volume as the key output variables of the remediation strategies. In addition, the influence of the dispersivities and the imposed hydraulic gradient upon the same output variables is critically evaluated. The results of the study illustrate the need for uncertainty reduction in the knowledge of the hydrogeologic parameters.

Simultaneous parameter estimation and contaminant source characterization for coupled groundwater flow and contaminant transport modelling

Parameter estimation and contaminant source characterization are key steps in the development of a coupled groundwater flow and contaminant transport simulation model. Here a methodologyfor simultaneous model parameter estimation and source characterization is presented. The parameter estimation/source characterization inverse model combines groundwater flow and contaminant transport simulation with non-linear maximum likelihood estimation to determine optimal estimates of the unknown model parameters and source characteristics based on measurements of hydraulic head and contaminant concentration. First-order uncertainty analysis provides a means for assessing the reliability of the maximum likelihood estimates and evaluating the accuracy and reliability of the flow and transport model predictions. A series of hypothetical examples is presented to demonstrate the ability of the inverse model to solve the combined parameter estimation/source characterization inverse problem. Hydraulic conductivities, effective porosity, longitudinal and transverse dispersivities, boundary flux, and contaminant flux at the source are estimated for a two-dimensional groundwater system. In addition, characterization of the history of contaminant disposal or location of the contaminant source is demonstrated. Finally, the problem of estimating the statistical parameters that describe the errors associated with the head and concentration data is addressed. A stage-wise estimation procedure is used to jointly estimate these statistical parameters along with the unknown model parameters and source characteristics.

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.