Contaminant source and release history identification in groundwater: A multi-step approach

Gzyl, Grzegorz; Zanini, A.; Frączek, R.; Kura, Karol

doi:10.1016/j.jconhyd.2013.11.006

Cited by 48 publications

(15 citation statements)

References 32 publications

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“…Contamination distribution along a CP can be converted to a concentration time series using analytical approaches [18] or numerical methods [19]. The IPT method can calculate mass flux along one CP [20], estimate natural attenuation rate between two CPs [21], and backtrack source information upstream from the CP [22,23]. The IPT method can be used for contamination characterization in combination with other methods [24,25].…”

Section: Introductionmentioning

confidence: 99%

Localization of Groundwater Contaminant Sources Using Artificially Enhanced Catchment

Zhao

Zhang

Chen

et al. 2020

Water

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The localization of groundwater contaminant sources is the first and most fundamental step when dealing with site contamination problems. This paper presents a novel approach for groundwater pollution source identification in a site with low-velocity groundwater in which a strategy of an artificially enhanced catchment is adopted. The distance from a pumping well (artificial sink) to the source zone (unknown source) is calculated by integrating the concentration time series and well flow. Then, the orientation of the source well is delineated by applying the method of distance intersection. This method is suitable for an approximately homogeneous aquifer with average hydraulic conductivity ranging from 1 × 10−6 to 1 × 10−5 m/s and a contaminant whose transport process can be generalized to convective migration. The developed method was applied to an industrial contaminated site using three pumping wells and two observation wells. The results demonstrated that four potential source positions were identified. Among these, two positions situated near the production workshop were excluded by observation well responses. The remaining two positions located near the drain were identified as preferred treatment sources. Electrical prospecting showed source orientation consistent with the artificially enhanced catchment results. This proved that the method is effective and provides an alternative tool to help solve the problem of source identification in the first stage of remediation in sites with low-velocity groundwater.

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Section: Introductionmentioning

confidence: 99%

Localization of Groundwater Contaminant Sources Using Artificially Enhanced Catchment

Zhao

Zhang

Chen

et al. 2020

Water

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“…Sciortino et al (2000) formulated inverse modeling for locating DNAPL pools in groundwater as a least squares minimization problem, solved by a search procedure based on the Levenberg-Marquardt method. Gzyl et al (2014) performed integral pumping tests to identify the locations of DNAPL sources in southern Poland and applied a geostatistical approach to identify their release history.…”

Section: Introductionmentioning

confidence: 99%

Application of Mixed-Integer Nonlinear Optimization Programming Based on Ensemble Surrogate Model for Dense Nonaqueous Phase Liquid Source Identification in Groundwater

HouZeyu

DaiZhenxue

LaoWangmei

et al. 2019

Environmental Engineering Science

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Groundwater contamination source identification (GCSI) is critical for taking effective measures to protect groundwater resources, assess risks, mitigate disasters, and design remediation strategies. Simulation-optimization techniques have been effective tools for GCSI. However, previous studies have applied individual surrogate models when replacing simulation models, rather than making efforts to combine various methods to improve the approximation accuracy of the surrogate model over the simulation model. In this study, the kernel extreme learning machine (KELM) model was proposed to enhance the surrogate model, and to approach GCSI problems, especially those of dense nonaqueous phase liquid-contaminated aquifers, more effectively. In addition, a kriging model and a support vector regression (SVR) model were built and compared with the KELM model, and various ensemble surrogate (ES) modeling techniques were applied to establish four ES models. Results showed that the KELM model was more accurate than the kriging and SVR models; however, the ES models performed much better than the three individual surrogate models. The most precise ES model increased the certainty coefficient (R 2 ) to 0.9837, whereas limiting the maximum relative error to 13.14%. Finally, a mixedinteger nonlinear programming optimization model was established to identify the groundwater contamination source in terms of location and release history, and simultaneously assess aquifer parameters. The ES model developed in this article could reasonably predict the system response under given operation conditions. Replacement of the simulation model by the ES model considerably reduced the computation burden of the simulation-optimization process and simultaneously achieved high computation accuracy.

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“…Gzyl et al . [] has developed a multistep approach to identify the source and its release history; this approach consists of three steps: performing integral pumping tests, identifying sources, and recovering the release history by means of a geostatistical approach.…”

Section: Introductionmentioning

confidence: 99%

Joint identification of contaminant source location, initial release time, and initial solute concentration in an aquifer via ensemble Kalman filtering

Gómez‐Hernández

2016

Water Resources Research

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When a contaminant is detected in a drinking well, source location, initial contaminant release time, and initial contaminant concentration are, in many cases, unknown; the responsible party may have disappeared and the identification of when and where the contamination happened may become difficult. Although contaminant source identification has been studied extensively in the last decades, we propose—to our knowledge, for the first time—the use of the ensemble Kalman filter (EnKF), which has proven to be a powerful algorithm for inverse modeling. The EnKF is tested in a two‐dimensional synthetic deterministic aquifer, identifying, satisfactorily, the source location, the release time, and the release concentration, together with an assessment of the uncertainty associated with this identification.

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Contaminant source and release history identification in groundwater: A multi-step approach

Cited by 48 publications

References 32 publications

Localization of Groundwater Contaminant Sources Using Artificially Enhanced Catchment

Localization of Groundwater Contaminant Sources Using Artificially Enhanced Catchment

Application of Mixed-Integer Nonlinear Optimization Programming Based on Ensemble Surrogate Model for Dense Nonaqueous Phase Liquid Source Identification in Groundwater

Joint identification of contaminant source location, initial release time, and initial solute concentration in an aquifer via ensemble Kalman filtering

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