Modeling the Efficiency of the Iron Coprecipitation-Filtration Process for the Removal of Arsenate at Low Initial Concentrations

Modeling the efficiency of arsenate removal at low initial arsenic (As) concentrations is a new challenge following the new maximum contaminant level (MCL) of As in drinking water, revised downward from 50 to 10 μg/L by the U.S. EPA. Many water systems across the United States are required to remove As from drinking water under the current regulations. However, most of the models used to predict As removal performance were developed and validated based on the old, higher concentration standard. This paper investigates and reports on the ability of a model, based on the diffuse double-layer (DDL) surface complexation model, to predict As removal for low As levels (10-20 μg/L). The model was validated with a pilot study using source water from Well No. 3 of the Mutual Domestic Water Consumers Association (MDWCA) in Anthony, New Mexico. Based on the comparison of experimental data with model results, the model presented here can successfully predict the efficiency of As removal by coprecipitation with iron (hydr)oxide when initial As concentration, total iron concentration, and pH are known. The average discrepancy between experimental data and predicted results ranged from 3 to 12%, as a function of conditions.