Advances in freshwater risk assessment: improved accuracy of dissolved organic matter-metal speciation prediction and rapid biological validation.

ABSTRACT

Speciation modeling of bioavailability has increasingly been used for environmental risk assessment (ERA). Heavy metal pollution is the most prevalent environmental pollution issue globally, and metal bioavailability is strongly affected by its chemical speciation. Dissolved organic matter (DOM) in freshwater will bind heavy metals thereby reducing bioavailability. While speciation modeling has been shown to be quite effective and is validated for use in ERA, there is an increasing body of literature reporting problems with the accuracy of metal-DOM binding in speciation models. In this study, we address this issue for a regional-scale field area (Lake Tai, with 2,400 km2 surface area and a watershed of 36,000 km2) where speciation models in common use are not highly accurate, and we tested alternative approaches to predict metal-DOM speciation/bioavailability for lead (Pb) in this first trial work. We tested five site-specific approaches to quantify Pb-DOM binding that involve varying assumptions about conditional stability constants, binding capacities, and different components in DOM, and we compare these to what we call a one-size-fits-all approach that is commonly in use. We compare model results to results for bioavailable Pb measured using a whole-cell bioreporter, which has been validated against speciation models and is extremely rapid compared to many biological methods. The results show that all of the site-specific approaches we use provide more accurate estimates of bioavailability than the default model tested, however, the variation of the conditional stability constant on a site-specific basis is the most important consideration. By quantitative metrics, up to an order of magnitude improvement in model accuracy results from modeling active DOM as a single organic ligand type with site-specific variations in Pb-DOM conditional stability constants. Because the biological method is rapid and parameters for site-specific tailoring of the model may be obtained via high-throughput analysis, the approach that we report here in this first regional-scale freshwater demonstration shows excellent potential for practical use in streamlined ERA.