Simultaneous identification of the pollutant release history and the source location in groundwater by means of a geostatistical approach

Butera, Ilaria; Tanda, Maria Giovanna; Zanini, A.

doi:10.1007/s00477-012-0662-1

Cited by 63 publications

(48 citation statements)

References 24 publications

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“…Other authors considered aquifer heterogeneity during inversion, using two‐dimensional flow and transport models [e.g., Bagtzoglou et al ., ; Saenton and Illangasekare , ]. There are studies using (geo)statistical methods to treat the uncertainty of both the unknown source function and the aquifer structure [e.g., Snodgrass and Kitanidis , ; Michalak and Kitanidis , ; Sun , ; Troldborg et al ., ; Butera et al ., ], but the geostatistical CSA description does not honor multiphase physics. In recent works, Zeng et al .…”

Section: Introductionmentioning

confidence: 99%

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Identification of contaminant source architectures—A statistical inversion that emulates multiphase physics in a computationally practicable manner

Koch

Nowak

2016

Water Resources Research

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The goal of this work is to improve the inference of nonaqueous-phase contaminated source zone architectures (CSA) from field data. We follow the idea that a physically motivated model for CSA formation helps in this inference by providing relevant relationships between observables and the unknown CSA. Typical multiphase models are computationally too expensive to be applied for inverse modeling; thus, state-of-the-art CSA identification techniques do not yet use physically based CSA formation models. To overcome this shortcoming, we apply a stochastic multiphase model with reduced computational effort that can be used to generate a large ensemble of possible CSA realizations. Further, we apply a reverse transport formulation in order to accelerate the inversion of transport-related data such as downgradient aqueous-phase concentrations. We combine these approaches within an inverse Bayesian methodology for joint inversion of CSA and aquifer parameters. Because we use multiphase physics to constrain and inform the inversion, (1) only physically meaningful CSAs are inferred; (2) each conditional realization is statistically meaningful; (3) we obtain physically meaningful spatial dependencies for interpolation and extrapolation of point-like observations between the different involved unknowns and observables, and (4) dependencies far beyond simple correlation; (5) the inversion yields meaningful uncertainty bounds. We illustrate our concept by inferring three-dimensional probability distributions of DNAPL residence, contaminant mass discharge, and of other CSA characteristics. In the inference example, we use synthetic numerical data on permeability, DNAPL saturation and downgradient aqueous-phase concentration, and we substantiate our claims about the advantages of emulating a multiphase flow model with reduced computational requirement in the inversion. Key Points:Physically based stochastic multiphase model supports CSA inference and proper handling of scales Statistical consistency is maintained in a stochastic Bayesian framework Valuable detailed information about the unknown contaminant source architecture is revealed PUBLICATIONS dimensional inverse problem. It is high-dimensional because the involved unknowns are the entire threedimensional distribution of DNAPL saturation within the source zone, and finely resolved three-dimensional fields of aquifer parameters need to be included as indispensable covariates [Koch and Nowak, 2015]. The data available for inversion may comprise a variety of data types such as soil properties, hydraulic heads, groundwater velocities, locations of known pure-phase contaminant residence, and dissolved-phase concentration values. It is a nonlinear problem, because the relationships between observables and the unknowns are nonlinear. The nonlinearity is especially pronounced since multiphase flow is a strongly nonlinear and, in this specific case, even an unstable process [Illangasekare et al., 1995;Glass, 2003]. Nonuniqueness means that an infinite set of realizations, each of...

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Section: Introductionmentioning

confidence: 99%

“…Using a random space function for the contaminant release, the stochastic inversion approaches of Michalak and Kitanidis [], Butera et al . [], and Hosseini et al . [] also infer information on the geometry or size of the source zone in a two‐dimensional aquifer with heterogeneous transmissivities.…”

Section: Introductionmentioning

confidence: 99%

Identification of contaminant source architectures—A statistical inversion that emulates multiphase physics in a computationally practicable manner

Koch

Nowak

2016

Water Resources Research

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“…The equation is a linear partial differential equation, because the equation is linear to all the unknown function and their derivatives [12]. When the contamination transport process is linear, the contamination concentration could be described by the transfer function as follows [8,13]:…”

Section: Transfer Function Considering the First-order Reactionmentioning

confidence: 99%

“…Michalak and Kitanidis [7] combine the method with the adjoint state method to identify the source function in a 3D problem. Butera et al [8] extend the method to find both the source function and location. Though this method is extensively studied, most of these researches give their attention on the conservative pollutants, the PSI method considering the chemical reaction is seldom discussed.…”

Section: Introductionmentioning

confidence: 99%

A geostatistical approach to groundwater pollution source identification considering first order reaction

Long¹,

Cui²,

Li³

et al. 2018

dwt

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a b s t r a c tChemical reaction is a very important factor of the groundwater pollution source identification (PSI). However, the PSI method based on the geostatistics is always applied on the conservative pollutants. In this paper, the finite difference is employed to obtain the transfer function of complex transference of pollutant in groundwater, and a PSI method considering the first-order reaction is proposed. A numerical test is employed to analyse the result of the new method and the impact of reaction rate on the PSI problem. In the case, the new method could identify the release process perfectly. Accurate PSI result could be obtained under high concentration or low chemical reaction consumption of pollution. Though the PSI result is insensitive to the reaction rate when the reaction rate is between 10 -4 and 10 -3 , the more accurate reaction rate is still very important for the PSI problem. The method prompted in this paper has good agreement with the transport rule of pollutant, and could be very helpful for identifying groundwater pollution.

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“…Tartakovsky (2007); Winter and Tartakovsky (2008), and Bolster et al (2009) analyzed the probability of aquifer contamination based on rare event approximations of the system's parts failing. Wang and Jin (2013) applied a Markov Chain Monte Carlo scheme to infer the possible location and magnitude of a contamination source, and Butera et al (2013) presented a geostatistical approach to identify the location and release history of a pollutant into an aquifer.…”

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confidence: 99%

Stochastic contaminant transport monitoring in heterogeneous sand and gravel aquifers of the United Arab Emirates

Paleologos

Papapetridis

Kendall

2014

Stoch Environ Res Risk Assess

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Stochastic numerical simulations were performed to investigate the evolution of plumes from pulse sources that can result from accidental leaks. The stochastic advection-dispersion equation was solved for hydraulic conductivities typical to the heterogeneous sandy and gravel aquifers encountered in the United Arab Emirates. Dispersivities were similar to those found in field studies at sandy aquifers, such as those conducted at Borden and Cape Cod, and at the Vejen, Denmark tracer tests. Our work showed that the detection probability, P d , of a monitoring network was affected strongly by the medium's dispersivity with a large number of wells (larger than 12) required, even in relatively simple geological environments, in order to detect contaminants with confidence. Monitoring systems following minimum regulatory requirements in terms of the number of wells were able to detect contamination at best in only one out of five cases. The frequency of sampling did not appear to be critical when the dispersivity was low and bi-annual sampling appeared to be satisfactory. In highly dispersive media monthly sampling was needed in order to increase detection. Increase of a medium's dispersivity in relative homogeneous aquifers reduced the performance of large well-systems to less than 50 %. Strongly heterogeneous and dispersive subsurface environments led all monitoring systems to fail in detection, irrespectively of frequency of sampling. Finally, large contaminant quantities did not improve the detection capabilities of low density wellsystems with detection enhancements restricted to high density ones.

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Simultaneous identification of the pollutant release history and the source location in groundwater by means of a geostatistical approach

Cited by 63 publications

References 24 publications

Identification of contaminant source architectures—A statistical inversion that emulates multiphase physics in a computationally practicable manner

Identification of contaminant source architectures—A statistical inversion that emulates multiphase physics in a computationally practicable manner

A geostatistical approach to groundwater pollution source identification considering first order reaction

Stochastic contaminant transport monitoring in heterogeneous sand and gravel aquifers of the United Arab Emirates

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