A dynamic model using monitoring data and watershed characteristics to project fish tissue mercury concentrations in stream systems.

A complex interplay of factors determines the degree of bioaccumulation of Hg in fish in any particular basin. Although certain watershed characteristics have been associated with higher or lower bioaccumulation rates, the relationships between these characteristics are poorly understood. To add to this understanding, a dynamic model was built to examine these relationships in stream systems. The model follows Hg from the water column, through microbial conversion and subsequent concentration, through the food web to piscivorous fish. The model was calibrated to 7 basins in Kentucky and further evaluated by comparing output to 7 sites in, or proximal to, the Ohio River Valley, an underrepresented region in the bioaccumulation literature. Water quality and basin characteristics were inputs into the model, with tissue concentrations of Hg of generic trophic level 3, 3.5, and 4 fish the output. Regulatory and monitoring data were used to calibrate and evaluate the model. Mean average prediction error for Kentucky sites was 26%, whereas mean error for evaluation sites was 51%. Variability within natural systems can be substantial and was quantified for fish tissue by analysis of the US Geological Survey National Fish Database. This analysis pointed to the need for more systematic sampling of fish tissue. Analysis of model output indicated that parameters that had the greatest impact on bioaccumulation influenced the system at several points. These parameters included forested and wetlands coverage and nutrient levels. Factors that were less sensitive modified the system at only 1 point and included the unfiltered total Hg input and the portion of the basin that is developed.

Development and evaluation of a dynamic model that projects population biomarkers of methylmercury exposure from local fish consumption.

A dynamic model was developed to project Hg concentrations in common biomarkers of exposure in response to changes in Hg concentrations in predatory fish from local waters. The model predicts biomarkers in susceptible populations for intake rates representing the mean, 90th, 95th, and 99 th percentiles of populations of interest. The biomarkers the model calculates are blood methylmercury, total hair Hg, and fetal blood methylmercury. Decision makers can use the model to determine the degree of reduction in fish tissue Hg levels necessary to protect the health of susceptible populations. Biomarker output was calibrated with literature sources. Output was then compared to additional literature sources to evaluate model function. Projected biomarkers were not different from literature sources. The model can be used as a tool to understand the impact of local fish consumption on susceptible populations.