Development of an Empirical Nonlinear Model for Mercury Bioaccumulation in the South and South Fork Shenandoah Rivers of Virginia

Mercury is a globally distributed pollutant that biomagnifies in aquatic food webs. In the United States, 3781 water bodies fail to meet criteria for safe fish consumption due to mercury bioaccumulation. In the risk assessment and management of these impairments (through the total maximum daily load program), an important step is evaluating the relationship between aqueous mercury and mercury in fish tissue. Often, this relationship is simplified to a bioaccumulation factor (BAF): the ratio of fish tissue mercury to aqueous mercury. This article evaluates the relationship between aqueous mercury and fish tissue mercury across a contamination gradient in the South and South Fork Shenandoah rivers of Virginia. The relationship was found to be nonlinear, with BAFs decreasing as the level of contamination increased. This means that protective water column mercury concentration targets established from site-specific BAFs will be overestimated in contaminated areas and will not be sufficiently protective. To avoid this over-prediction in the South and South Fork Shenandoah rivers, an empirical nonlinear Michaelis–Menten model was used to establish a protective water-quality target. Among other models and variables, the Michaelis–Menten model, relating total mercury in the water column to methylmercury in fish tissue, achieved the best empirical fit (r2 = 0.9562). The resulting water-quality targets using this model were 3.8 and 3.2 ng/l for the South and South Fork Shenandoah rivers, respectively. These values are 2.1–2.5 times lower than the water-quality target developed using a site-specific BAF. These findings demonstrate the need to consider nonlinear BAF relationships in mercury-contaminated areas.

[1]  R C Back,et al.  Bioaccumulation of mercury in pelagic freshwater food webs. , 1998, The Science of the total environment.

[2]  L. J. Carter Chemical plants leave unexpected legacy for two virginia rivers. , 1977, Science.

[3]  M Craig Barber,et al.  Application of ecosystem‐scale fate and bioaccumulation models to predict fish mercury response times to changes in atmospheric deposition , 2009, Environmental toxicology and chemistry.

[4]  M. Winfrey,et al.  Environmental factors affecting the formation of methylmercury in low pH lakes , 1990 .

[5]  E. A. Henry,et al.  Mercury methylation in aquatic systems affected by acid deposition. , 1991, Environmental pollution.

[6]  Evaluating regional predictive capacity of a process‐based mercury exposure model, regional‐mercury cycling model, applied to 91 Vermont and New Hampshire lakes and ponds, USA , 2007, Environmental toxicology and chemistry.

[7]  John A. Sorensen,et al.  Airborne mercury deposition and watershed characteristics in relation to mercury concentrations in water, sediments, plankton, and fish of eighty Northern Minnesota lakes , 1990 .

[8]  D. DeForest,et al.  Assessing metal bioaccumulation in aquatic environments: the inverse relationship between bioaccumulation factors, trophic transfer factors and exposure concentration. , 2007, Aquatic toxicology.

[9]  James M Skeaff,et al.  Inverse relationship between bioconcentration factor and exposure concentration for metals: Implications for hazard assessment of metals in the aquatic environment , 2003, Environmental toxicology and chemistry.

[10]  Samuel N Luoma,et al.  Why is metal bioaccumulation so variable? Biodynamics as a unifying concept. , 2005, Environmental science & technology.

[11]  T. Augspurger,et al.  Impacts of mercury contamination in the southeastern United States , 1995 .

[12]  R. Bartha,et al.  Enzymatic catalysis of mercury methylation by Desulfovibrio desulfuricans LS , 1994, Applied and environmental microbiology.

[13]  D. Burk,et al.  The Determination of Enzyme Dissociation Constants , 1934 .

[14]  F. Morel,et al.  Bioaccumulation of mercury and methylmercury , 1995 .

[15]  Reed C. Harris,et al.  Temperature, growth and dietary effects on fish mercury dynamics in two Ontario lakes , 1998 .

[16]  R. Bartha,et al.  Mercury Methylation and Demethylation in Anoxic Lake Sediments and by Strictly Anaerobic Bacteria , 1998, Applied and Environmental Microbiology.

[17]  N. Bloom,et al.  Mercury and methylmercury, in individual zooplankton: Implications for bioaccumulation , 1992 .

[18]  M. Peterson,et al.  Bioaccumulation Factors for Mercury in Stream Fish , 2004 .

[19]  J. Eggleston Mercury Loads in the South River and Simulation of Mercury Total Maximum Daily Loads (TMDLs) for the South River, South Fork Shenandoah River, and Shenandoah River: Shenandoah Valley, Virginia , 2009 .

[20]  J. Pizzuto,et al.  Distribution, behavior, and transport of inorganic and methylmercury in a high gradient stream. , 2010 .

[21]  F. Morel,et al.  THE CHEMICAL CYCLE AND BIOACCUMULATION OF MERCURY , 1998 .

[22]  R. Bartha,et al.  Metabolic Pathways Leading to Mercury Methylation in Desulfovibrio desulfuricans LS , 1994, Applied and environmental microbiology.

[23]  B. Hope A basin‐specific aquatic food web biomagnification model for estimation of mercury target levels , 2003, Environmental toxicology and chemistry.

[24]  T. Clarkson The three modern faces of mercury. , 2002, Environmental health perspectives.

[25]  H. Lodish Molecular Cell Biology , 1986 .

[26]  M. Harada,et al.  Minamata disease: methylmercury poisoning in Japan caused by environmental pollution. , 1995, Critical reviews in toxicology.

[27]  D. Krabbenhoft,et al.  A National Pilot Study of Mercury Contamination of Aquatic Ecosystems along Multiple Gradients , 1999 .

[28]  C. Driscoll,et al.  Mercury in Freshwater Fish of Northeast North America – A Geographic Perspective Based on Fish Tissue Monitoring Databases , 2005, Ecotoxicology.