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The present research work explores different types of machine learning algorithms to estimate the water quality index and the water quality class. The samples were collected from Korattur Lake in Chennai city and tested for its necessary hydro-chemical parameters. The machine learning models such as support vector machine, decision tree, logistic regression, random forest, and naive Bayesian for assessing the water quality with respect to the accuracy and precision of the model. The water quality parameters such as pH, total dissolved salts, turbidity, phosphate, nitrate, iron, chemical oxygen demand, chloride, and sodium are used as a raw dataset. The models are then tested and evaluated to find the best suitable model by comparing and analyzing the accuracy of prediction, the precision rate, and the time taken for execution of all the models. Among all the algorithms employed, the random forest algorithm produces 95% accuracy which is the highest and also consumes the least execution time. From the random forest model, it was found that water quality has 84% of contamination which was attributed to unfit for drinking purpose. Hence, it could be suggested that water quality left disturbed due to anthropogenic activities and improper maintenance.

The past decades showed much research interest in lowering the fluoride ion content in water due to its hazardous nature. Fluoride present in water is likely to have favorable and unfavorable consequences to human life. It depends upon the permissible limits prescribed by the agencies, which paves the way to opt for fluoridation or defluoridation techniques. The excess intake of fluoride by human beings provides many issues that include deformities in skeletal structure, dental fluorosis, weakening of the nervous system, and severe damage to the kidney and liver. This review paper addresses the issues caused due to the over-dosage of fluoride and by compiling the recognized treatment techniques adopted to eliminate it from aqueous solution. The current study also figures out the versatility of every technique by revealing its pros and cons and bridges the gap between the emerging advancements to the practical mode of application. Several pieces of literature have been taken into consideration to withdraw the limitations of the key issues. This review paper suggests that adsorption technology can be adopted due to its simplicity and flexibility to overcome the technological issues. It is one such technique which enhances the defluoridation in trace amount and can be easily implemented in the rural areas.

In this present study, tin (Sn) doped MnFe2O4(Sn0.15Mn0.85Fe2O4) nanocomposites were synthesized using a microwave combustion method by employing Alocasia odutraplant extract as a natural fuel. Sn is well incorporated with spinel ferrite (MnFe2O4) which is confirmed by XRD analysis. FE-SEM analysis indicates that the synthesized sample having a rod-like structure in nature. The emerging pharmaceutical contaminant, namely, diclofenac (2-(2-(2,6-dichlorophenylamino)phenyl) acetic acid) (DCF), the non-steroidal anti-inflammatory drug was utilized as a model pollutant to evaluate the catalytic efficiency of the Sn0.15Mn0.85Fe2O4(SMF) under solar irradiation. SMF shows higher catalytic activity than pristine MnFe2O4due to a reduction in the bandgap energy from 1.78 eV to 1.54 eV as a result of Sn doping into MnFe2O4. The degradation efficiency of DCF using the SMF catalyst was found to be 99% in a shorter interval (50 min) with 71% mineralization. Radical trapping experiments indicate that the photo-generated holes and hydroxyl radicals actively participate in the degradation process of DCF using the SMF catalyst. The optimum pH for the removal of DCF using the SMF catalyst was found to be 4 with the catalyst dosage of 0.8 g/L. The recycling experiment shows that the SMF catalyst has high stability up to five cycles and low iron leaching ability under optimum conditions. Sn0.15Mn0.85Fe2O4catalyst can be recovered using an external magnet from the reaction mixture and it is recycled. It was concluded that the synthesized catalyst can be used as a tertiary treatment option for the removal of emerging pharmaceutical pollutants.