SPATIALLY DISTRIBUTED BIOACCUMULATION RISK ANALYSIS: A GIS-BASED TOOL AND A CASE STUDY OF POLYCHLORINATED BIPHENYLS IN THE GREAT LAKES

Abstract

This thesis presents a GIS-based tool Arc-BEST (Bioaccumulation Evaluation Screening Tool) to perform spatially distributed bioaccumulation risk analyses. Estimating bioaccumulation risk is important to help predict potentially adverse effects from contaminants on ecosystems and human health, which are key factors in the development of sound public policy. Arc-BEST is based on the BEST model in the U.S. Army Corps of Engineers BRAMS (Bioaccumulation Risk Assessment Modeling System) software, released in 2012. It predicts concentration of concern contaminants in predators tissues from concentrations in organisms at the bottom of the food chain. It also estimates carcinogenic and non-carcinogenic risks for humans that consume those species. The new tool is easy to use, requires few parameters, and is flexible to modify the food chain structure and exposure scenarios. The greatest contribution of Arc-BEST is that it enables the automated use of digital spatial data sets, which improves model creation speed and the analysis, comparison and visualization of results. Furthermore, the model was improved to consider up to four trophic levels. The code for Arc-BEST is written in Python, is open-source, and can also be used as a stand-alone model called by other software programs. In this work Arc-BEST is proposed to be used as part of a screening-level risk assessment process in order to identify hot spots where further studies and monitoring should be performed to ensure humans and ecosystems health. The tool is successfully applied to a case study of PCBs in the Laurentian Great Lakes, where long-term effects of PCBs is performed, based on concentrations in zebra mussels (Dreissena polymorpha). Zebra mussels have a great filtration capacity and high bioconcentration rates, increasing the bioavailability of contaminants for predator species. PCBs concentrations in different-level predators are predicted. Furthermore, health risks for humans that consume sport fish are estimated for different exposure scenarios. The distribution of the risks in the different lakes is analyzed, and critical areas are identified.