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MODELING OF METAL BINDING ON HUMIC SUBSTANCES USING THE NIST DATABASE: AN A PRIORI FUNCTIONAL GROUP APPROACH
Citation:
Zhang, Z. Z. AND G W. Bailey. MODELING OF METAL BINDING ON HUMIC SUBSTANCES USING THE NIST DATABASE: AN A PRIORI FUNCTIONAL GROUP APPROACH. Presented at Seventh International Symposium on Fish Physiology, Toxicology, and Water Quality, Tallinn, Estonia, May 12-15, 2003.
Impact/Purpose:
Elucidate and model the underlying processes (physical, chemical, enzymatic, biological, and geochemical) that describe the species-specific transformation and transport of organic contaminants and nutrients in environmental and biological systems. Develop and integrate chemical behavior parameterization models (e.g., SPARC), chemical-process models, and ecosystem-characterization models into reactive-transport models.
Description:
Various modeling approaches have been developed for metal binding on humic substances. However, most of these models are still curve-fitting exercises-- the resulting set of parameters such as affinity constants (or the distribution of them) is found to depend on pH, ionic strength, and concentrations of metals and humic substances. Consequently, these models are not satisfactory to predict metal binding under environmental settings. We have developed an a priori model based on the elemental composition and functional group concentrations of humic substances, using the National Institute of Standard and Technology (NIST) database of critically selected stability constants of metal complexes. We tabulated the stability constants of metal complexes with selected functional groups and have plotted the corresponding conditional stability constants at several pH levels. These data showed that in addition to oxygen-bearing groups, the nitrogen-bearing groups and sulfur-bearing groups are also important for metal binding. The amino acid group plays a significant role for binding of Cu(II), Hg(II), Cr(III) and Fe(III), whereas the SH-functional group is important for the binding of soft Lewis acids' metals, such as Cd(II), Hg(II), and Pb(II). We have shown that such a simple model is capable of predicting adsorption and competitive adsorption of metals when the concentration of metals is below 10(-5) to 10(-6) M, which is the relevant metal concentration under most environmental settings.