Alkaline leachate, dust generation, and foul smell during the stacking process of natural rubber biosludge (NRBS) can pollute surrounding water, soil, and air. In this study, natural rubber chemically activated carbon (NRCAC) has been synthesized for the first time from NRBS by pyrolysis using ZnCl at 700 °C for adsorptive removal of Cr(VI) and methylene blue (MB) from aqueous solutions. Both NRBS and NRCAC were characterized by X-ray fluorescence (XRF), X-ray diffraction (XRD), Fourier transform infrared (FTIR), scanning electron microscopy-energy dispersive spectroscopy (SEM-EDS), Brunauer-Emmett-Teller (BET), and thermogravimetric analyzer (TGA).
View Article and Find Full Text PDFGroundwater contamination becomes an alarming threat to the provision of ecosystem services and natural resources. A very high level of groundwater contamination has been observed in the northeastern states particularly in North Tripura district. Therefore, the present study considered the region as a case study to evaluate the hydrogeochemical facies, heavy metal pollution and irrigation indices, and their impact on human health.
View Article and Find Full Text PDFThe adsorptive removal of Cr(VI) and methylene blue (MB) was studied in a batch reactor using activated carbon (RAC), prepared from natural rubber waste, along with the commercial activated carbon (CAC). Maximum uptake of Cr(VI) and MB by the RAC was 21 and 30 mg g, respectively, whereas the corresponding uptake by CAC was 145 and 224 mg g. The kinetics of adsorption, however, was found to be faster in RAC than CAC.
View Article and Find Full Text PDFA modeling and simulation study was carried out on a new flux-enhancing and solar-driven membrane distillation module for removal of arsenic from contaminated groundwater. The developed new model was validated with rigorous experimental investigations using arsenic-contaminated groundwater. By incorporating flash vaporization dynamics, the model turned out to be substantially different from the existing direct contact membrane distillation models and could successfully predict (with relative error of only 0.
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