Investigating on soil–bentonite liners for controlling heavy metal migration

Abstract This research enhances the understanding of contaminant migration in bentonite-amended soil liners, aiding in the creation of more efficient pollution control strategies. The study focuses on deriving breakthrough curves for lead and iron ions using locally sourced soil samples mixed with 10% bentonite. Through soil column tests, experimental breakthrough curves are generated and then matched with theoretical curves derived from the advection–diffusion equation. The modelling of curves is done with analytical solutions, enabling the calculation of diffusion coefficients by considering Ogata Bank’s Method, Time Lag Method and Root time methods. Transport parameters such as diffusion rates and retardation factors are iteratively adjusted to align the theoretical models with experimental data. This approach not only confirms the transport parameters but also enhances our capability to predict breakthrough times for different liner thicknesses. The innovation lies in combining local soil properties with bentonite amendments and fine-tuning theoretical models with experimental findings. The research demonstrated that soil liners amended with bentonite significantly decreased the movement of lead and iron ions, improving containment efficiency by about 40% for lead and 35% for iron compared to unamended soil. These findings highlight the effectiveness of bentonite-amended soil liners in reducing heavy metal contamination.

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Publication Details

Journal
Multiscale and Multidisciplinary Modeling Experiments and Design
Published
2026-07-22
DOI
https://doi.org/10.1007/s41939-026-01243-5
Primary Topic
Landfill Environmental Impact Studies
Type
article
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article

Investigating on soil–bentonite liners for controlling heavy metal migration

Megha Kulkarni, Makodu Puttaswamygowda Pavithra, N. R. Abhilash
Multiscale and Multidisciplinary Modeling Experiments and Design
Landfill Environmental Impact Studies
article

Investigating on soil–bentonite liners for controlling heavy metal migration

Megha Kulkarni, Makodu Puttaswamygowda Pavithra, N. R. Abhilash
article en

Abstract

Abstract This research enhances the understanding of contaminant migration in bentonite-amended soil liners, aiding in the creation of more efficient pollution control strategies. The study focuses on deriving breakthrough curves for lead and iron ions using locally sourced soil samples mixed with 10% bentonite. Through soil column tests, experimental breakthrough curves are generated and then matched with theoretical curves derived from the advection–diffusion equation. The modelling of curves is done with analytical solutions, enabling the calculation of diffusion coefficients by considering Ogata Bank’s Method, Time Lag Method and Root time methods. Transport parameters such as diffusion rates and retardation factors are iteratively adjusted to align the theoretical models with experimental data. This approach not only confirms the transport parameters but also enhances our capability to predict breakthrough times for different liner thicknesses. The innovation lies in combining local soil properties with bentonite amendments and fine-tuning theoretical models with experimental findings. The research demonstrated that soil liners amended with bentonite significantly decreased the movement of lead and iron ions, improving containment efficiency by about 40% for lead and 35% for iron compared to unamended soil. These findings highlight the effectiveness of bentonite-amended soil liners in reducing heavy metal contamination.

Multiscale and Multidisciplinary Modeling Experiments and DesignVol. 9(1)
Bangur Institute of Neurosciences (IN), Visvesvaraya Technological University (IN), REVA University (IN)
Openalex Percentile: Top 9%
Landfill Environmental Impact Studies
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Investigating on soil–bentonite liners for controlling heavy metal migration — Megha Kulkarni, Makodu Puttaswamygowda Pavithra, et al. · Multiscale and Multidisciplinary Modeling Experiments and Design (2026) | TGRS Research Map | TGRS