Mechanochemically Activated Aluminosilicate Clay Soils and their Application for Defluoridation and Pathogen Removal from Groundwater.
Olumuyiwa A ObijoleWilson Mugera GitariPatrick Gathura NdunguAmidou SamiePublished in: International journal of environmental research and public health (2019)
In this study, aluminosilicate rich clay soils were prepared through mechanochemical activation. The chemical and mineralogical properties were investigated using X-Ray Fluorescence (XRF) and X-ray diffraction (XRD). The functional groups, morphology and surface area were evaluated using Fourier Transform Infra-Red (FTIR), Scanning electron microscopy (SEM) and Brunauer-Emmett-Teller (BET) analysis. Batch experiments were used to evaluate its defluoridation efficiency while antibacterial activities were assessed using well diffusion method. Maximum adsorption capacity was found to be 1.87 mg/g with 32% fluoride removal. Fluoride adsorption was found to reduce in the presence of Cl-, PO₄2- and CO₃2- while it increased in the presence of SO₄2- and NO₃-. Adsorption data fitted well to Freundlich isotherms, hence, confirming heterogeneous multilayer adsorption. Kinetic studies revealed that fluoride adsorption fitted well to pseudo-second order model. The sorption of F- onto the clays' surface followed intra-particle diffusion mode. High correlation coefficient indicates that the sorption process was greatly controlled by particle diffusion while it is minimal in pore diffusion model. Antibacterial studies revealed no zone of inhibition for all the activated clays, hence indicating that they are not active against the bacterial strains of Escherichia coli used in this study. The results showed activated clays' potential for defluoridation. Its effectiveness in pathogen removal is limited. Hence further modifications of the clays' surfaces are hereby recommended.
Keyphrases
- electron microscopy
- escherichia coli
- aqueous solution
- drinking water
- heavy metals
- human health
- high resolution
- randomized controlled trial
- risk assessment
- magnetic resonance imaging
- mass spectrometry
- case control
- electronic health record
- pseudomonas aeruginosa
- machine learning
- dual energy
- artificial intelligence
- deep learning
- contrast enhanced