Nanotechnology has been widely used in many fields including in soil and groundwater remediation. Nanoremediation has emerged as an effective, rapid, and efficient technology for soil and groundwater contaminated with petroleum pollutants and heavy metals. This review provides an overview of the application of nanomaterials for environmental cleanup, such as soil and groundwater remediation. Four types of nanomaterials, namely nanoscale zero-valent iron (nZVI), carbon nanotubes (CNTs), and metallic and magnetic nanoparticles (MNPs), are presented and discussed. In addition, the potential environmental risks of the nanomaterial application in soil remediation are highlighted. Moreover, this review provides insight into the combination of nanoremediation with other remediation technologies. The study demonstrates that nZVI had been widely studied for high-efficiency environmental remediation due to its high reactivity and excellent contaminant immobilization capability. CNTs have received more attention for remediation of organic and inorganic contaminants because of their unique adsorption characteristics. Environmental remediations using metal and MNPs are also favorable due to their facile magnetic separation and unique metal-ion adsorption. The modified nZVI showed less toxicity towards soil bacteria than bare nZVI; thus, modifying or coating nZVI could reduce its ecotoxicity. The combination of nanoremediation with other remediation technology is shown to be a valuable soil remediation technique as the synergetic effects may increase the sustainability of the applied process towards green technology for soil remediation.

The decomposition of municipal solid waste (MSW) in landfills under anaerobic conditions produces landfill gas (LFG) containing approximately 50—60% methane (CH₄) and 30—40% carbon dioxide (CO₂) by volume. CH₄ has a global warming potential 21 times greater than CO₂; thus, it poses a serious environmental problem. As landfills are the main method for waste disposal in Malaysia, the major aim of this study was to estimate the total CH₄ emissions from landfills in all Malaysian regions and states for the year 2009 using the IPCC, 1996 first-order decay (FOD) model focusing on clean development mechanism (CDM) project applications to initiate emission reductions. Furthermore, the authors attempted to assess, in quantitative terms, the amount of CH₄ that would be emitted from landfills in the period from 1981—2024 using the IPCC 2006 FOD model. The total CH₄ emission using the IPCC 1996 model was estimated to be 318.8 Gg in 2009. The Northern region had the highest CH₄ emission inventory, with 128.8 Gg, whereas the Borneo region had the lowest, with 24.2 Gg. It was estimated that Pulau Penang state produced the highest CH₄ emission, 77.6 Gg, followed by the remaining states with emission values ranging from 38.5 to 1.5 Gg. Based on the IPCC 1996 FOD model, the total Malaysian CH ₄ emission was forecast to be 397.7 Gg by 2020. The IPCC 2006 FOD model estimated a 201 Gg CH₄ emission in 2009, and estimates ranged from 98 Gg in 1981 to 263 Gg in 2024.

Knowing the fraction of methane (CH₄) oxidized in landfill cover soils is an important step in estimating the total CH₄ emissions from any landfill. Predicting CH₄ oxidation in landfill cover soils is a difficult task because it is controlled by a number of biological and environmental factors. This study proposes an artificial neural network (ANN) approach using feedforward backpropagation to predict CH₄ oxidation in landfill cover soil in relation to air temperature, soil moisture content, oxygen (O₂) concentration at a depth of 10 cm in cover soil, and CH₄ concentration at the bottom of cover soil. The optimum ANN model giving the lowest mean square error (MSE) was configured from three layers, with 12 and 9 neurons at the first and the second hidden layers, respectively, log-sigmoid (logsig) transfer function at the hidden and output layers, and the Levenberg-Marquardt training algorithm. This study revealed that the ANN oxidation model can predict CH₄ oxidation with a MSE of 0.0082, a coefficient of determination (R ²) between the measured and predicted outputs of up to 0.937, and a model efficiency (E) of 0.8978. To conclude, further developments of the proposed ANN model are required to generalize and apply the model to other landfills with different cover soil properties.