Recovery the release history and source location of a pollutant in groundwater using data collected in laboratory

This work shows the application of an innovative procedure that is able to simultaneously identify the release history and the source location of a pollutant injection in groundwater using a dataset obtained experimentally. The methodology follows a geostatistical approach and it requires a preliminary delineation of a probably source area. The dataset was provided through an experimental installation developed at the hydraulic laboratory of the University of Parma (DICATeA). The equipment represents a 2-D unconfined aquifer controlled through two constant head levels (upstream and downstream); it consists of a Plexiglas sandbox filled with a porous medium (1 mm glass beads). An injector was placed inside the porous medium and sodium fluorescein salt was used as tracer during the tests. The standard test consists of releasing a constant and known concentration with a variable flow rate. The injection rate and the mean flow rate inside the sandbox are stored by means of a data acquisition system, meanwhile the concentration distribution inside the sandbox is observed through the processing of side wall images collected by means of a digital camera. The digital camera and the sandbox are placed in a dark room lightened by blue light in order to excite the fluorescein and easily evaluate the concentration distribution. A Matlab routine was developed to cut and to correct images by a projective transformation in order to obtain pictures with same size and orientation. Each pixel of the image has known coordinates on the sandbox. After a calibration process, the relationships between the luminosity of the emitted fluorescence and the tracer concentration have been identified in each pixel of the picture and consequently in each point of the domain. Initially a series of simple tests (with constant injection) were carried out with the aim at validating the experimental equipment comparing the observed data to those collected through the images, such as mass balance or mass flow rate. Once that the equipment was considered reliable, the tracer was injected with a variable flow rate in order to test and validate a geostatistical procedure that it is able to simultaneously recover the release history and the source location with a dataset provided under known and controlled condition. 20 concentration values at different times, obtained from the photographic technique, of 2 monitoring points were used to recover the flow rate injected in the porous media in time. A numerical model was developed to support the procedure, in particular, considering a constant injection, it allowed to identify the transfer functions between the source and the monitoring points. At first only the true source was considered and the injected flow rate was well recovered. Then the release history was recovered simultaneously for 4 potential sources (one true and three false). The geostatistical approach showed at the true source the actual release history and null concentration at the other sources. This demonstrated the capability of the method and the reliability of the experimental equipment

Contaminant release history identification in 2-D heterogeneous aquifers through a minimum relative entropy approach

The minimum relative entropy (MRE) method has been applied in a wide variety of fields since it was first introduced. Woodbury and Ulrych (Water Resour Res 29(8): 2847–2860, 1993, Water Resour Res 32(9): 2671–2681, 1996) adopted and improved this method to solve linear inverse problems in aquifers. In this work, the MRE method was improved to detect the source release history in 2-D aquifer characterized by a non-uniform flow-field. The approach was tested on two cases: a 2-D homogeneous conductivity field and a heterogeneous one (the hydraulic conductivity presents three orders of magnitude in terms of variability). In the latter case the transfer function cannot be described with an analytical formulation, thus, the transfer functions were estimated by means of a numerical procedure. In order to analyze the method performance in different conditions, two datasets have been used: observations collected at the same time at 20 different monitoring points, and observations collected at 2 monitoring points at several times. The observed data have been processed with and without a random error and the Boxcar and Gaussian probability distribution functions were considered as a priori information. The agreement between the true and the estimated data has been evaluated through the calculation of the normalized Root Mean Square error. The approach was able to recover the release history even in the most difficult case