Abstract
This paper presents a GIS-based tool named 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, and corresponding bioaccumulation factors. Additionally, it estimates carcinogenic and non-carcinogenic risks for humans that consume those species. The greatest contribution of Arc-BEST is that it enables the automated use of digital spatial data sets, which improves model creation speed, analysis and visualization of results, and comparison and cross-referencing with other geographic datasets. Furthermore, the model was improved to consider up to four trophic levels. The code is written in Python and is open-source. In this work Arc-BEST is 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 in the Laurentian Great Lakes, where long-term effects of polychlorinated biphenyls (PCBs) is performed, based on measured concentrations in zebra mussels (Dreissena polymorpha), and local bioaccumulation factors from previous studies. 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 various exposure scenarios. The distribution of the risks in the lakes is analyzed, and critical areas are identified.
Highlights
The environment is continuously loaded with man-made organic chemicals and metals released by urban communities and industries
This paper presents a Geographic Information System (GIS)-based tool named Arc-BEST (Bioaccumulation Evaluation Screening Tool) to perform spatially distributed bioaccumulation risk analyses
The tool is successfully applied to a case study in the Laurentian Great Lakes, where long-term effects of polychlorinated biphenyls (PCBs) is performed, based on measured concentrations in zebra mussels (Dreissena polymorpha), and local bioaccumulation factors from previous studies
Summary
The environment is continuously loaded with man-made organic chemicals and metals released by urban communities and industries. Examples of these chemicals are polychlorinated biphenyls (PCBs), organochlorine pesticides, polycyclic aromatic hydrocarbons (PAHs), dioxins, and mercury. The ultimate destination for many of these contaminants is the aquatic environment, either due to direct discharges or to hydrologic and atmospheric processes [1] They can represent potential risks for human health and for both aquatic and terrestrial ecosystems due to the mechanism of bioaccumulation. The objective of this work is to develop a Geographic Information System (GIS)-based tool to estimate bioaccumulation of contaminants in aquatic ecosystems, and the health risks for humans that feed on these species. A clear example is the elaboration of safe fishing guides and update of fish consumption advisories, which currently exist for some fish in the Great Lakes [10]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
