Derivation of an ambient water quality criterion for mercury: Taking account of site‐specific conditions

Abstract

Mercury is considered to be a serious risk to wildlife. As a result, the Great Lakes Water Quality Initiative and others have developed ambient water quality criteria (AWQC) for the protection of wildlife. These AWQC have been controversial, however, because the AWQC were single values that did not account for site-specific conditions, derivation of the AWQC relied on a single no-observed-adverse-effect level, and the AWQC had an unknown level of conservatism because of reliance on both average and conservative assumptions and uncertainty factors. Rather than develop a single-value AWQC for total mercury, we derived an AWQC model that explicitly incorporates factors controlling bioavailability, methylation rates, and bioaccumulation in the aquatic environment (e.g., pH, dissolved organic carbon). To derive our AWQC model, field data were collected from 31 lakes in Ontario and an additional 10 lakes in Nova Scotia (North America). In the field study, levels of total and methylmercury in water and fish as well as levels of key water quality variables were determined. We conducted multiple-regression analysis to derive a model that estimates mercury levels in prey of mink. Mink are very sensitive to mercury exposure. An independent dataset consisting of 51 water bodies in the United States was then used to confirm the validity and robustness of the AWQC model. Next, we combined the results of chronic-feeding studies with similar protocols and endpoints in a meta-analysis to derive a dose-response curve for mink exposed to mercury in the diet. In the final step, we used a probabilistic risk model to estimate the concentrations of methylmercury in water that would lead to levels in fish sufficient for a 10% probability of exceeding the lethal dose affecting 5% of the mink population. The result is an AWQC equation for mercury for the protection of wildlife that can be used with a variety of site-specific conditions.

Recommendations for the development and application of wildlife toxicity reference values

“…It is more likely that TRVs will be generated using single dose–response curves. Receptor‐specific models can be developed when sufficient data exist (e.g., Kerr and Meador 1996; Moore et al 1997, 1999, 2003; Wayland et al 2007). In cases where data are more limited, it may be possible to combine dose and response data from different species or endpoints and use the whole data set for deriving the model.…”

“…In the first situation, there are several “joint probability” methods (e.g., Jacobs and Vesilind 1992; Solomon et al 2000; Suter 2007) that consider both the exposure and effects distributions to develop true risk estimates (i.e., both probability and magnitude of adverse effects). Some examples of studies where these techniques have been applied in wildlife ecological risk assessments include Moore et al (1997, 1999, 2003) and Wayland et al (2007). In cases where dose–response relationships were not modeled, the exposure distribution could be used to estimate the probability of exceeding one (or several) EDx values (i.e., derived using scatter plots).…”

Environmental Risks of Inorganic Metals and Metalloids: A Continuing, Evolving Scientific Odyssey

“…Metals accumulation is dependent on bioavailability, which is primarily determined by metal speciation (Campbell 1995;Kraemer et al 1996;Paquin et al 2002) as affected by site-specific conditions (Moore et al 2003) including metal-particle interactions (Merdy et al 2006), organism behavior , and, in some cases, transient protection via the composition of the cell wall of aquatic and terrestrial plants (García-Ríos et al 2007) or of bacteria (Claessens and van Cappellen 2007). Water hardness may be less important than dissolved organic carbon (DOC) concentrations in mediating metal bioaccumulation (Markich et al 2005).…”