Gerald E. Speitel Jr., Lynn E. Katz, Chia-Chen Chen, Shannon Stokes, Paul Westerhoff, and Pedram Shafieian

OBJECTIVES:

The overall goal of this research was to improve our ability to predict arsenic adsorption equilibria under different water quality conditions, thereby improving our ability to predict bed service life by combining the new adsorption equilibria understanding with mass transport considerations. Specifically, the project focused on calibrating the diffuse layer model (DLM) and coupling it to the Homogenous Surface Diffusion Model (HSDM) to predict adsorption in packed bed reactors.

BACKGROUND:

New USEPA regulations reduced the Maximum Contaminant Level (MCL) to 10 µg/L in 2006. The new arsenic regulations will have the most dramatic impact on groundwater systems that commonly have minimal wellhead treatment systems. Packed bed treatment systems are well suited for smaller groundwater-based utilities requiring arsenic removal to meet the new MCL. As such, the focus of the proposed research is on the performance of adsorptive, metal oxide, granular media in packed-bed treatment systems.

APPROACH:

Surface site density, pH_pzc, and the surface acidity constants for each adsorbent were estimated by tritium exchange and potentiometric titration experiments. The potentiometric titration data and single-solute adsorption data were used to calibrate the DLM surface complexation reactions using FITEQL 4.0. The DLM parameters were evaluated for their ability to predict adsorption data collected in batch experiments with simultaneous addition of competing solutes. Rapid small scale column tests (RSSCTs) were also conducted under controlled conditions for the purpose of generating continuous-flow data for calibration and verification of the linked equilibrium/mass transport model. To this end, a major change was made to the HSDM in this research by incorporating the DLM to describe multicomponent adsorption equilibria in place of ideal adsorbed solution theory (IAST) and Freundlich isotherms.

RESULTS/CONCLUSIONS:

Batch equilibrium adsorption data were collected for As(V), V(V), Si, and Ca²⁺ on E33, granular ferric hydroxide (GFH), and granular titanium oxide (GTO) in single and multi-solute systems. Single solute adsorption equilibrium experiments indicated that the trends in As(V) adsorption capacity followed the pH_pzc of the adsorbents with E33>GFH>GTO. DLM predictions of the bi-solute experimental data provided insight into the operative mechanisms of interaction among the solutes. Because V(V) adsorbed more strongly than As(V) to all of the adsorbents, adding V(V) led to a significant reduction in As(V) adsorption on GFH and E33. The DLM also predicted the impact of Si and Ca²⁺ on As(V) adsorption for GFH and E33. The synergistic impacts of Ca²⁺ as well as the antagonistic impacts of Si led to reduced As(V) adsorption at low pH due to Si competition and increased adsorption at high pH due to electrostatic contributions from Ca²⁺.

The DLM constants calibrated from batch data were incorporated into the HSDM/DLM. The model was first fit to the single-component, pH 8.3 RSSCT data to estimate mass transport rate constants. The model then predicted the single-component, pH 6.0 RSSCT data, thereby demonstrating the potential for the HSDM/DLM to predict the impact of pH on As breakthrough.

APPLICATIONS/RECOMMENDATIONS:

The combined efforts from batch adsorption experiments and DLM modeling led to the development of a database for predicting As(V) adsorption in multi-solute systems on granular iron oxides. In addition, the multi-solute data and modeling results provide insight into the operative mechanisms of interaction among the solutes. The HSDM/DLM modeling using DLM parameters derived from batch experiments showed promise for predicting As(V) removal from packed bed adsorbents at different pH over a broad range of experimental conditions. These results can be used to guide the selection of packed bad adsorbents in treating As(V) contaminated groundwaters.

RESEARCH PARTNER:

This study was jointly funded by the Water Research Foundation and the U.S. Department of Energy through the Arsenic Water Technology Partnership. The report will also be published by WERC (a Consortium for Environmental Education and Technology Development at New Mexico State University).