Identification of leachate infiltration and its flow pathway in landfill by means of electrical resistivity tomography (ERT)

Helene, Lívia Portes Innocenti; Moreira, César Augusto; Bovi, Renata Cristina

doi:10.1007/s10661-020-8206-5

Cited by 29 publications

(11 citation statements)

References 23 publications

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“…This in turn enables the determination and mapping of contaminated soils in dumping sites. Therefore, the ERT method plays a major role in detecting the wastes and their contaminant plumes due to the high contrast of resistivity between the contaminating waste body and the natural environment (Appiah et al, 2018;Baawain et al, 2018;Helene et al, 2020). The choice of the suitable electrode configuration depends on the structure to be investigated, the noise level, and the sensitivity of the resistivity meter.…”

Section: Electrical Resistivity Tomography (Ert) Surveymentioning

confidence: 99%

“…Several geophysical techniques improved their power in detection the geometry of waste deposits in a faster, cheaper, and non-destructive way. Among these techniques are magnetic methods (Marchetti et al, 2002;Prezzi et al, 2005), electrical resistivity and electromagnetic prospecting (Baawain et al, 2018;Bernstone & Dahlin, 1997;Candansayar & Tezkan, 2006;De Carlo et al, 2013;Frangos, 1997;Helene et al, 2020;Karlik & Kaya, 2001;Martinho & Almeida, 2006;Morita et al, 2020;Saraev et al, 2020;Tezkan et al, 2000), ground penetration radar (GPR) (Orlando & Marchesi, 2001;Porsani et al, 2004;Wu & Huang, 2006), seismic methods (Carpenter et al 1991;De Laco et al, 2003;Missiaen & Feller, 2008) in addition to the use of a combination of more than one geophysical method (Appiah et al, 2018;Genelle et al, 2014;Tezkan et al, 1996;Yannah et al, 2019). Moreover, investigating leachates and locating possible paths of pollution plumes in landfill areas using near-surface geophysical techniques have been studied by several scientists (e.g.…”

Section: Introductionmentioning

confidence: 99%

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Integrated Interpretation of Magnetic and ERT Data to Characterize a Landfill in the North-West of Cologne, Germany

Ibraheem

Tezkan

Bergers

2021

Pure Appl. Geophys.

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Electrical resistivity tomography (ERT) and ground magnetic surveys were applied to characterize an old uncontrolled landfill in a former exploited sand and gravel quarry in an area to the north-west of the city of Cologne, Germany. The total magnetic field and its vertical gradient were recorded using a proton precession magnetometer to cover an area of about 43,250 m2. The magnetic data were transferred to the frequency domain and then reduced to the north magnetic pole. The amplitude of the analytical signal was calculated to define the magnetic materials within and outside the landfill. Eight ERT profiles were constructed based on the results of the magnetic survey using different electrode arrays (Wenner, dipole–dipole, and Schlumberger). In order to increase both data coverage and sensitivity and to decrease uncertainty, a non-conventional mixed array was used. The subsurface resistivity distributions were imaged using the robust (L1-norm) inversion method. The resultant inverted subsurface true resistivity data were presented in the form of 2D cross sections and 3D fence diagram. These non-invasive geophysical tools helped us to portray the covering soil, the spatial limits of the landfill, and the depth of the waste body. We also successfully detected low resistivity zones at deeper depths than expected, which probably be associated with migration pathways of the leachate plumes. The findings of the present study provide valuable information for decision makers with regards to environmental monitoring and assessment.

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Section: Electrical Resistivity Tomography (Ert) Surveymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Integrated Interpretation of Magnetic and ERT Data to Characterize a Landfill in the North-West of Cologne, Germany

Ibraheem

Tezkan

Bergers

2021

Pure Appl. Geophys.

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“…Geoelectrical methods are commonly applied for mining exploration, geotechnical characterization and hydrological studies (Pelton et al ., 1976; Sumner, 1976; Goldie, 2002; Loke et al ., 2013). Electrical resistivity tomography (ERT) is usually applied for groundwater exploration, geotechnical characterization, mapping of contaminant plumes, landfill and levees monitoring (Uhlemann et al ., 2017; Tresoldi et al ., 2018, 2019; Hojat et al ., 2019; Helene et al ., 2020). Typically, deterministic gradient‐based algorithms (Zhang et al ., 2005; Pidlisecky and Knight, 2008) are employed to tackle the ERT problem.…”

Section: Introductionmentioning

confidence: 99%

“…. Electrical resistivity tomography (ERT) is usually applied for groundwater exploration, geotechnical characterization, mapping of contaminant plumes, landfill and levees monitoring (Uhlemann et al, 2017;Tresoldi et al, 2018Tresoldi et al, , 2019Hojat et al, 2019;Helene et al, 2020). Typically, deterministic gradient-based algorithms (Zhang et al, 2005;Pidlisecky and Knight, 2008) are employed to tackle the ERT problem.…”

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confidence: 99%

Discrete cosine transform for parameter space reduction in Bayesian electrical resistivity tomography

Vinciguerra

Aleardi

Hojat

et al. 2021

Geophysical Prospecting

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Electrical resistivity tomography is a non‐linear and ill‐posed geophysical inverse problem that is usually solved through gradient‐descent methods. This strategy is computationally fast and easy to implement but impedes accurate uncertainty appraisals. We present a probabilistic approach to two‐dimensional electrical resistivity tomography in which a Markov chain Monte Carlo algorithm is used to numerically evaluate the posterior probability density function that fully quantifies the uncertainty affecting the recovered solution. The main drawback of Markov chain Monte Carlo approaches is related to the considerable number of sampled models needed to achieve accurate posterior assessments in high‐dimensional parameter spaces. Therefore, to reduce the computational burden of the inversion process, we employ the differential evolution Markov chain, a hybrid method between non‐linear optimization and Markov chain Monte Carlo sampling, which exploits multiple and interactive chains to speed up the probabilistic sampling. Moreover, the discrete cosine transform reparameterization is employed to reduce the dimensionality of the parameter space removing the high‐frequency components of the resistivity model which are not sensitive to data. In this framework, the unknown parameters become the series of coefficients associated with the retained discrete cosine transform basis functions. First, synthetic data inversions are used to validate the proposed method and to demonstrate the benefits provided by the discrete cosine transform compression. To this end, we compare the outcomes of the implemented approach with those provided by a differential evolution Markov chain algorithm running in the full, un‐reduced model space. Then, we apply the method to invert field data acquired along a river embankment. The results yielded by the implemented approach are also benchmarked against a standard local inversion algorithm. The proposed Bayesian inversion provides posterior mean models in agreement with the predictions achieved by the gradient‐based inversion, but it also provides model uncertainties, which can be used for penetration depth and resolution limit identification.

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“…Among these geophysical methods, electrical resistivity measurement has been recognized as a powerful tool to support hydrogeological investigations (Binley and Slater, 2020). It has been applied to a wide range of hydrogeology problems (Mondal et al, 2010;Helene et al, 2020;Aranda et al, 2021). For example, Lesparre et al (2019) conducted a hydrogeophysical experiment and used 4D ERT to characterize the aquifer thermal energy storage.…”

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confidence: 99%

Effects of grain size, solution salinity and pH on the electrical response of oil‐bearing carbonate sands

Deng

Qian

Luo

et al. 2022

Near Surface Geophysics

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Researchers around the world have carried out numerous studies on the electrical response of oil‐contaminated silica sands. However, few studies consider the oil contamination in carbonate sands that exhibit quite different characteristics to silica sands. The purpose of this work is to study the influence of grain size (0.5–1.0, 1–3 and 3–5 mm), solution salinity (100, 10 and 1 Ω·m) and pH (4.58, 6.5 and 9.25) on the resistivity and saturation exponent of oil‐bearing carbonate sands. Experimental columns were packed with crushed limestone grains, solution and oil. Electrical resistivity measurements were subsequently performed on the packed columns. Grain size exhibits a complex effect on the electrical response of partially saturated limestone grains. Resistivity decreases slightly with increasing grain size when water saturation is greater than 0.4, while it shows an inconsistent trend when water saturation is lower than 0.4. The resulting saturation exponent n decreases slightly when grain size increases from 0.5–1 mm to 1–3 mm, while it shows a positive change in n values for grain size of 3–5 mm. Changes in solution salinity significantly influence the resistivity and saturation exponent of limestone grains, with higher solution salinity resulting in lower resistivity and higher saturation exponent. However, with solution pH increasing from 4.58 to 9.25, it shows very limited effect on the resistivity and saturation exponent of limestone grains. This can be attributed to the strong buffering ability of limestone. Effects of grain size, solution salinity and pH on the resistivity and saturation exponent of oil‐bearing carbonate sands were investigated. Resistivity is found to show a decreasing trend with increasing grain size from 0.5–1 mm to 3–5 mm above a certain saturation (e.g. Sw > 0.4), while it shows a rapid increase below a certain saturation (e.g., Sw≤$ \le $0.4) for a larger grain size of 3–5 mm. Resistivity decreases and the saturation exponent increases with the increase of solution salinity for oil‐bearing carbonate sands. Resistivity and saturation exponent do not vary significantly with solution pH ranging from 4.58 to 9.25 for oil‐bearing carbonate sands.

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Identification of leachate infiltration and its flow pathway in landfill by means of electrical resistivity tomography (ERT)

Cited by 29 publications

References 23 publications

Integrated Interpretation of Magnetic and ERT Data to Characterize a Landfill in the North-West of Cologne, Germany

Integrated Interpretation of Magnetic and ERT Data to Characterize a Landfill in the North-West of Cologne, Germany

Discrete cosine transform for parameter space reduction in Bayesian electrical resistivity tomography

Effects of grain size, solution salinity and pH on the electrical response of oil‐bearing carbonate sands

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