Evaluation of silver nanoparticles (AgNPs) penetration through a clay liner in landfills

Lee, Young-Su; Kim, Yeong Min; Lee, Jongkeun; Kim, Jae Young

doi:10.1016/j.jhazmat.2020.124098

Cited by 7 publications

(4 citation statements)

References 63 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Lee et al addressed the question whether AgNPs can penetrate through clay liners used in landfills . Landfill liner conditions were simulated in a controlled laboratory study, and field soil and bentonite were tested in batch sorption experiments.…”

Section: Nanomaterialsmentioning

confidence: 99%

Water Analysis: Emerging Contaminants and Current Issues

2021

View full text Add to dashboard Cite

Section: Nanomaterialsmentioning

confidence: 99%

Water Analysis: Emerging Contaminants and Current Issues

2021

View full text Add to dashboard Cite

“…These involve the hydrologic assessment of a landfill model [2][3][4][5], which is the equation of Richard for one-phase unsaturated flow through homogeneous and heterogeneous porous media [6][7][8]. Another method used for analyzing solute transport in landfills is a convective-dispersive equation, which uses the transport and transformation processes of dispersion, advection, and sorption in unsaturated porous media and chemical and biological transformation [9][10][11][12]. The main disadvantage of such models is the fact that they require a high number of parameters based on chemical, hydraulic, and biological characteristics.…”

Section: Introductionmentioning

confidence: 99%

Investigating Landfill Leachate and Groundwater Quality Prediction Using a Robust Integrated Artificial Intelligence Model: Grey Wolf Metaheuristic Optimization Algorithm and Extreme Learning Machine

Alizamir

Kazemi

Kermani

et al. 2023

Water

View full text Add to dashboard Cite

The likelihood of surface water and groundwater contamination is higher in regions close to landfills due to the possibility of leachate percolation, which is a potential source of pollution. Therefore, proposing a reliable framework for monitoring leachate and groundwater parameters is an essential task for the managers and authorities of water quality control. For this purpose, an efficient hybrid artificial intelligence model based on grey wolf metaheuristic optimization algorithm and extreme learning machine (ELM-GWO) is used for predicting landfill leachate quality (COD and BOD5) and groundwater quality (turbidity and EC) at the Saravan landfill, Rasht, Iran. In this study, leachate and groundwater samples were collected from the Saravan landfill and monitoring wells. Moreover, the concentration of different physico-chemical parameters and heavy metal concentration in leachate (Cd, Cr, Cu, Fe, Ni, Pb, Mn, Zn, turbidity, Ca, Na, NO3, Cl, K, COD, and BOD5) and in groundwater (Cd, Cr, Cu, Fe, Ni, Pb, Mn, Zn, turbidity, EC, TDS, pH, Cl, Na, NO3, and K). The results obtained from ELM-GWO were compared with four different artificial intelligence models: multivariate adaptive regression splines (MARS), extreme learning machine (ELM), multilayer perceptron artificial neural network (MLPANN), and multilayer perceptron artificial neural network integrated with grey wolf metaheuristic optimization algorithm (MLPANN-GWO). The results of this study confirm that ELM-GWO considerably enhanced the predictive performance of the MLPANN-GWO, ELM, MLPANN, and MARS models in terms of the root-mean-square error, respectively, by 43.07%, 73.88%, 74.5%, and 88.55% for COD; 23.91%, 59.31%, 62.85%, and 77.71% for BOD5; 14.08%, 47.86%, 53.43%, and 57.04% for turbidity; and 38.57%, 59.64%, 67.94%, and 74.76% for EC. Therefore, ELM-GWO can be applied as a robust approach for investigating leachate and groundwater quality parameters in different landfill sites.

show abstract

“…Studies have been conducted on the effects of different materials on loess stabilization and seepage control. Silver nanoparticles (AgNPs) have been evaluated by Lee et al 10 for their ability to penetrate a clay liner in landfills. The results indicate that AgNPs cannot easily pass through the liner.…”

Section: Introductionmentioning

confidence: 99%

Study on the relationship between permeability coefficient and porosity, the confining and osmotic pressure of attapulgite-modified loess

Zhang,

Pishro,

Zhang

et al. 2023

Sci Rep

View full text Add to dashboard Cite

This study investigated attapulgite-modified loess as an efficient and cost-effective method for creating an impermeable liner for landfills in regions with scarce clay resources. Laboratory permeability tests were conducted using a flexible wall permeameter to determine the permeability of compacted loess and attapulgite mixtures under varying osmotic conditions. The relationship between the permeability coefficient, attapulgite dosage, radial pressure, and osmotic pressure was analyzed. Nuclear magnetic resonance and scanning electron microscopy were also used to observe the microstructure of the modified loess. The results showed that attapulgite dosage significantly reduced the permeability coefficient, but the effect became limited when the content surpassed 10%. The decrease of the permeability coefficient of the modified loess is mainly due to the filling of pores between the loess by attapulgite, which makes the pore size and throat size of the modified loess smaller. The modified loess displayed a sheet structure that contributed to an increased permeability coefficient due to increased radial pressure. This study provides valuable insights into using attapulgite-modified loess as a material for landfill lining in regions with scarce clay resources.

show abstract

Evaluation of silver nanoparticles (AgNPs) penetration through a clay liner in landfills

Cited by 7 publications

References 63 publications

Water Analysis: Emerging Contaminants and Current Issues

Water Analysis: Emerging Contaminants and Current Issues

Investigating Landfill Leachate and Groundwater Quality Prediction Using a Robust Integrated Artificial Intelligence Model: Grey Wolf Metaheuristic Optimization Algorithm and Extreme Learning Machine

Study on the relationship between permeability coefficient and porosity, the confining and osmotic pressure of attapulgite-modified loess

Contact Info

Product

Resources

About