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SURFACE COMPLEXATION MODEL TO DESCRIBE COMPETITIVE ARSENIC ADSORPTION ONTO IRON OXIDES
Impact/Purpose:
Advances in technology for removing arsenic from drinking water are of critical importance both in the U.S. and worldwide. Research into means of low-cost treatment for removal of arsenic has led to the development of a “solar oxidation and removal of arsenic” (SORAS) process in which sunlight, lemon juice, and air are used to remove arsenic from drinking water. Because the system relies only on these three things, it holds promise for low-cost arsenic removal in places such as Bangladesh where there is widespread arsenic contamination in groundwater but little large-scale treatment of drinking water.
It is difficult, however, to predict how much arsenic can be removed because of the interference of background water chemistry on the arsenic adsorption processes. Recent work at the University of Texas at Austin has attempted to develop a surface complexation and mass transport model for the removal of arsenic in a packed bed of granular ferric hydroxide. The model should allow prediction of arsenic removal based on known groundwater characteristics in the same way that existing models predict removal of organic contaminants by granular activated carbon. Developing a similar model to predict the effectiveness of SORAS would need to account for the effects of silica, carbonate, calcium, and citrate.
The objective of this research is to use both equilibrium adsorption data and spectroscopic studies to develop such a model for SORAS. The model would describe arsenic adsorption onto amorphous ferric hydroxide in a multi-solute system.
Description:
The output of this work would be a set of self-consistent model parameters that would describe the adsorption of arsenic onto iron oxides in the presence of other solutes. The model could be used to predict the potential for removal of arsenic from drinking water by adsorption onto iron oxides and the expected effectiveness of the system based on background water chemistry.