Northeastern Section (45th Annual) and Southeastern Section (59th Annual) Joint Meeting (13-16 March 2010)

Paper No. 10
Presentation Time: 11:25 AM


KRABBENHOFT, David1, BOOTH, Nathan1, LUTZ, Michelle1, FIENEN, Michael N.2 and SALTMAN, Tamara3, (1)Wisconsin Water Science Center, U.S. Geological Survey, 8505 Research Way, Middleton, WI 53562, (2)Wisconsin Water Science Center, 8505 Research Way, Middleton, WI 53562, (3)U.S. Environmental Protection Agency, Office of Air and Radiation, 1200 Pennsylvania Avenue, N. W, Washington, DC 20460,

About 20 years ago, researchers at a few locations across the globe discovered high levels of mercury in fish from remote settings lacking any obvious mercury source. We now know that for most locations atmospheric deposition is the dominant mercury source, and that mercury methylation is the key process that translates low mercury loading rates into relatively high levels in top predators of aquatic food webs. Despite significant advances in our understanding of the controlling processes, and national-scale image of relative contamination levels has remained elusive. As such, resource managers and public health officials have limited options for informing the public on of where elevated mercury concentrations in sport fish are more likely to occur than others. This project provides, for the first time, a national map of predicted (modeled) methylmercury concentrations in surface waters. The map shows clear regional trends. East of the Mississippi, the Gulf and southeastern Atlantic coasts, the northeast, the lower Mississippi valley, and Great Lakes area are predicted to have generally higher environmental methylmercury levels. Higher-elevation, well-drained areas of Appalachia are predicted to have relatively lower methylmercury abundance. Other than the prairie pothole region, in the western US incessant regional patterns are less clear. However, the full range of predicted methylmercury levels is predicted to occur in western US watersheds. Lastly, although this map is being presented at the continental US scale, the principles used to generate the modeled results can easily applied to data sets that represent ranges (local to global) in geographic scales.