Recent U.S. Environmental Protection Agency (USEPA) regulatory changes have reduced the Maximum Contaminant Level (MCL) for arsenic from 50 to 10 µg/L. The new arsenic regulations will have the most dramatic impact on groundwater systems that commonly have minimal wellhead treatment systems, and generally have higher arsenic concentrations than surface waters. Arsenic can be removed by adsorption on metal (hydr)oxides during coagulation or by adsorptive packed-beds, ion exchange resins, and membrane separation systems (McNeill and Edwards 1995; Scott et al. 1995; McNeill and Edwards 1997a,b; Brandhuber and Amy 1998, 2001; Chen et al. 2002; Wang et al. 2002; Wingrich and Wolf 2002; DeMarco et al. 2003). Packed-bed adsorption systems are usually cost-effective, have minimal waste streams, and are relatively simple to operate. Although the use of granular oxide media in continuous-flow packed beds is an attractive option for utilities, the actual performance of these technologies can vary by more than a factor of 1000 in terms of run length to breakthrough, and currently no basis exists for predicting these variations without expensive trial and error testing at pilot scale or at bench scale using rapid small-scale column tests (RSSCTs). Moreover, once the experimental testing phase is completed, the results apply only to the exact water tested, and they cannot be used to confidently predict the changes that will occur in response to even slight changes in water quality (e.g., pH or arsenic speciation). Thus, a robust modeling approach, analogous to what exists for removal of organic chemicals on granular activated carbon, is needed to complement and extend the available experimental approaches, as well as provide a conceptual framework for interpreting performance data. For a successful modeling approach, adsorption equilibrium must be described accurately within the matrices of other ions that are encountered in practice. Surface complexation modeling is the appropriate technique for characterizing single and multi-solute adsorption equilibrium in systems of this type. The focus of this research and presentation is on the development of diffuse layer model (DLM) and triple layer model (TLM) parameters for arsenate adsorption onto two commercially available iron based adsorbents (GFH and E33 Bayoxide) in background waters containing silica and calcium. Macroscopic batch sorption experiments were conducted over a range of surface coverage, pH values, and competing ion conditions. DLM and TLM were used to model As(V) uptake by the commercial sorbents examined to incorporate the enhancement and/or competition caused by the source water ions. These model parameters will form a self consistent database, similar to that developed for ferrihydrite by Dzombak and Morel (1990). The SCMs can then be integrated in a dynamic transport model to predict column life given a particular source water and to predict the effects of source water changes. The study determined parameters for the SCMs based on surface titration data, macroscopic batch adsorption experiments, and spectroscopic data. The models were calibrated using a limited set of the single solute experimental data, and then verified using the remaining data collected over the broader range of solute and solution conditions. Model calibration was performed using FITEQL 4.0, a commonly used parameter estimation software package for SCMs. Model verification was performed using MINEQL+ and/or FITEQL 4.0. Includes 10 references, figures.