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

  • Robert N. BrentEmail author
  • Donald G. Kain
Article

Abstract

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 (r 2 = 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.

Keywords

Mercury Concentration Methylmercury United States Environmental Protection Agency Total Mercury Fish Tissue 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  1. 1.Department of Integrated Science and TechnologyJames Madison UniversityHarrisonburgUSA
  2. 2.Virginia Department of Environmental QualityHarrisonburgUSA

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