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The most important and indispensable substance for life on earth is water. Regrettably, the growths of civilization, population, and industrssialization have led to a steady deterioration in the quality of available water resources. Heavy metals are a particular problem because of their high toxicity. This work highlights up-to-date methods for the removal of heavy metals from water using the technique of adsorption. After a comprehensive review of the range of materials used as adsorbents to remove heavy metals, the paper focuses on one particular technique, involving carbon nanotubes (CNTs). Tests have confirmed that CNTs are highly efficient, but have also revealed some drawbacks. Further adjustments and refinements are therefore required to improve their ability to remove heavy metals. This study shows that using deep eutectic solvents (DESs) as a functionalization agent to replace ionic liquids (ILs) has a number of advantages, including lower costs, easier availability of materials and environmentally friendliness. In sum, it suggests that the most efficient adsorption process is to use functionalized CNTs with DESs as an adsorbent. The critical analysis and conclusions emerging from this study should be of benefit to engineers, environmental scientists and chemists interested in the use of carbon nanotubes for use in environmental remediation. • Heavy metals removal using different techniques and materials. • Deep eutectic solvents (DESs) an environmentally friendly functionalization agent. • The applications in the nanotechnology for heavy metals removal. [ABSTRACT FROM AUTHOR]

• Decision tree model for water quality index prediction proposed in this study. • Authors identified the inter-relationship of WQI with different sets of parameters. • The proposed methodology is offering as cost-effective approach for WQI estimation. The development of water quality prediction models is an important step towards better water quality management of rivers. The traditional method for computing WQI is always associated with errors due to the protracted analysis of the water quality parameters in addition to the great effort and time involved in gathering and analyzing water samples. In addition, the cost of identifying the magnitude of some of the parameters through experimental testing is very high. The water quality of rivers in Malaysia is ranked into five classes based on water quality index (WQI). WQI is function of six water quality parameters: ammoniac nitrogen (NH 3 -N), biochemical oxygen demand (BOD), chemical oxygen demand (COD), dissolved oxygen (DO), pH, and suspended solids (SS). In this research, the decision tree machine learning technique is used to predict the WQI for the Klang River and its classification within a specific water quality class. Klang River is one of the most polluted rivers in Malaysia. Modeling experiments are designed to test the prediction and classification accuracy of the model based on various scenarios composed of different water quality parameters. Results show that the proposed prediction model has a promising potential to predict the class of the WQI. Moreover, the proposed model offers a more efficient process and cost-effective approach for the computation and prediction of WQI. [ABSTRACT FROM AUTHOR]

In the recent decade, deep eutectic solvents (DESs) have occupied a strategic place in green chemistry research. This paper discusses the application of DESs as functionalization agents for multi-walled carbon nanotubes (CNTs) to produce novel adsorbents for the removal of 2,4-dichlorophenol (2,4-DCP) from aqueous solution. Also, it focuses on the application of the feedforward backpropagation neural network (FBPNN) technique to predict the adsorption capacity of DES-functionalized CNTs. The optimum adsorption conditions that are required for the maximum removal of 2,4-DCP were determined by studying the impact of the operational parameters (i.e., the solution pH, adsorbent dosage, and contact time) on the adsorption capacity of the produced adsorbents. Two kinetic models were applied to describe the adsorption rate and mechanism. Based on the correlation coefficient (R2) value, the adsorption kinetic data were well defined by the pseudo second-order model. The precision and efficiency of the FBPNN model was approved by calculating four statistical indicators, with the smallest value of the mean square error being 5.01 × 10−5. Moreover, further accuracy checking was implemented through the sensitivity study of the experimental parameters. The competence of the model for prediction of 2,4-DCP removal was confirmed with an R2 of 0.99. [ABSTRACT FROM AUTHOR]