Optimal Dynamic Monitoring Network Design and Identification of Unknown Groundwater Pollution Sources

Abstract

The identification of unknown pollution sources is a prerequisite for designing of a remediation strategy. In most of the real world situations, it is difficult to identify the pollution sources without a scientifically designed efficient monitoring network. The locations of the contaminant concentration measurement sites would determine the efficiency of the unknown source identification process to a large extent. Therefore coupled and iterative sequential source identification and dynamic monitoring network design framework is developed. The coupled approach provides a framework for necessary sequential exchange of information between monitoring network and source identification methodology. The preliminary identification of unknown sources, based on limited concentration data from existing arbitrarily located wells provides the initial rough estimate of the source fluxes. These identified source fluxes are then utilized for designing an optimal monitoring network for the first stage. Both the monitoring network and source identification process is repeated by sequential identification of sources and design of monitoring network which provides the feedback information. In the optimal source identification model, the Jacobian matrix which is the determinant for the search direction in the nonlinear optimization model links the groundwater flow-transport simulator and the optimization method. For the optimal monitoring network design, the integer programming based optimal design model requires as input, simulated sets of concentration data. In the proposed methodology, the concentration measurement data from the designed and 2032 B. Datta et al.implemented monitoring network are used as feedback information for sequential identification of unknown pollution sources. The potential applicability of the developed methodology is demonstrated for an illustrative study area.

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

“…Some of the initial contributions in identification of unknown groundwater pollution sources proposed the use of linear optimization model based on linear response matrix approach (Gorelick et al, 1983) and statistical pattern recognition (Datta et al, 1989). Some of the important contributions to solve the unknown groundwater pollution sources identification problem include: non-linear maximum likelihood estimation based inverse models to determine optimal estimates of the unknown model parameters and source characteristics (Wagner, 1992); minimum relative entropy, a gradient based optimization for solving source identification problems (Woodbury et al, 1998); embedded nonlinear optimization technique for source identification (Mahar and Datta, 1997; inverse procedures based on correlation coefficient optimization (Sidauruk et al, 1997); Genetic Algorithm (GA) based approach (Aral et al, 2001;Singh & Datta, 2006); Artificial Neural Network (ANN) approach , 2007; constrained robust least square approach (Sun et al, 2006); classical optimization based approach (Datta et al, 2009a; inverse particle tracking approach (Bagtzoglou, 2003;Ababou et al, 2010); heuristic harmony search for source identification (Ayvaz, 2010); Simulated Annealing (SA) as optimization for source identification (Jha & Datta, 2011;Prakash & Datta, 2012, 2013, 2014a. A review of different optimization techniques for solving source identification problem is presented in Chadalavada et al (2011) and Amirabdollahian and Datta (2013).…”

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

“…To resolve the above-mentioned issue while the actual measurement of contaminant sources is missing, many researchers began to use the inverse solution method to identify groundwater pollution sources. A significant number of statistical and deterministic methods have been proposed to solve this inverse problem considering the hydrogeological conditions known [5][6][7][8][9][10]. Extensive reviews on the identification methods of pollution source characteristics and the applications of various inverse modeling techniques in pollution source identification have been described in past research [11][12][13][14].…”

Groundwater Contamination Plume Delineation Using Local Singularity Mapping Technique

“…In this method, the contamination source characteristics identified using available observed concentrations are utilized to design a new monitoring network. Then the new selected monitoring locations are used sequentially to improve the accuracy of identified source characteristics [3,14,[19][20][21].…”