A geostatistical approach to recover the release history of groundwater pollutants

Abstract

[1] In this paper the problem of recovering the temporal release history of a pollutant is approached with a geostatistical methodology that analyzes the pollutant concentration measured at a given time in the aquifer. The adopted methodology was developed by Snodgrass and Kitanidis [1997] for one-dimensional flow and transport. Here it is extended to the case of two-dimensional transport and additional improvements are carried out, with important consequences on technical applications. A literature numerical case study is used to assess the quality of the results and the performance of the algorithms with regard to (1) the plume sampling scheme, (2) the impact of concentration measurement errors, (3) the impact of errors on the estimated aquifer parameters, and (4) the erroneous identification of the hydraulic gradient direction. The new applications focus on the incorporation of nonpoint and multiple sources in order to quantify the relative legal liability of the different sources. The results of the numerical analysis show that the method provides a reasonable description of the release history and associated estimate error variance.INDEX TERMS: 1829 Hydrology: Groundwater hydrology; 1831 Hydrology: Groundwater quality; 1832 Hydrology: Groundwater transport; 1894 Hydrology: Instruments and techniques; KEYWORDS: pollutant release, groundwater pollution, inverse problem, geostatistical approach, transport and dispersion Citation: Butera, I., and M. G. Tanda, A geostatistical approach to recover the release history of groundwater pollutants, Water Resour.

Evaluation of dispersivity coefficients by means of a laboratory image analysis

“…In order to quantify this error, we carried out a comparison test with an analytical solution: at first we considered a 2-D analytical solution (Butera and Tanda, 2003) with a uniform effective velocity (u = 1.23°10 −3 m/s) and dispersivity values (α L = 1.06 mm, α T = 0.45 mm) equal to the one of our test case. A contaminant (at mass rate 0.105 mg/ s) was injected for 980 s and the plume evolution was simulated for a total of about 1600 s. Then, taken a window with the same dimension of the sandbox, we created "images", at different times, of the analytically determined concentration field on a grid with density equal to that of the images we processed in the test case.…”

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

“…Comprehensive review of source identification methodologies can be found in work by Atmadja and Bagtzoglou (2001b), Michalak and Kitanidis (2004), and Sun et al (2006a). Numerous works related to pollution source identification are available, like least square regression and linear programming with response matrix approach (Gorelick et al 1983), statistical pattern recognition (Datta et al 1989), random walk based backward tracking model (Bagtzoglou et al 1992), nonlinear maximum likelihood estimation (Wagner 1992), nonlinear optimization with embedding technique (Mahar and Datta 1997, 2001, correlation coefficient optimization (Sidauruk et al 1997), backward probabilistic model (Neupauer and Wilson 1999), geostatistical inversion approach (Snodgrass and Kitanidis 1997;Butera and Tanda 2003;Michalak and Kitanidis 2004), Tikhonov regularization (Skaggs and Kabala 1994;Liu and Ball 1999), quasi-reversibility (Skaggs and Kabala 1995;Bagtzoglou and Atmadja 2003), marching-jury backward beam equation (Atmadja and Bagtzoglou 2001a;Bagtzoglou and Atmadja 2003), genetic algorithm based approach (Aral et al 2001;Mahinthakumar and Sayeed 2005;Singh and Datta 2006), artificial neural network approach Datta 2004, 2007;, constrained robust least square approach (Sun et al 2006a, b), robust geostatistical approach (Sun 2007). However, only few studies have incorporated monitoring network within the source identification framework.…”

Reverse level pool routing: Comparison between a deterministic and a stochastic approach

“…In this section only a qualitatively description of the BGA is presented (for more mathematical details see Butera and Tanda, 2003;Hoeksema and Kitanidis, 1984;Kitanidis, 1995;Kitanidis and Vomvoris, 1983;Nowak and Cirpka, 2004;Snodgrass and Kitanidis, 1997).…”