A GIS-based tool for bioaccumulation risk analysis and its application to study polychlorinated biphenyls in the Great Lakes

Fernanda P. Maciel; Joshua M. Peschel; Fernanda P. Maciel; Joshua M. Peschel

doi:10.3934/environsci.2018.1.1

AIMS Environmental Science

2018, Volume 5, Issue 1: 1-23. doi: 10.3934/environsci.2018.1.1

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Research article

A GIS-based tool for bioaccumulation risk analysis and its application to study polychlorinated biphenyls in the Great Lakes

Fernanda P. Maciel ^{1
,
,},
Joshua M. Peschel ²

1.
Instituto de Mecánica de los Fluidos e Ingeniería Ambiental, Facultad de Ingeniería, Universidad de la República, Montevideo, Uruguay
2.
Agricultural and Biosystems Engineering, Iowa State University, Ames, IA, USA

Received: 14 October 2017 Accepted: 02 January 2018 Published: 16 January 2018

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.
- bioaccumulation,
- risk analysis,
- GIS,
- PCBs,
- Great Lakes,
- zebra mussels
Citation: Fernanda P. Maciel, Joshua M. Peschel. A GIS-based tool for bioaccumulation risk analysis and its application to study polychlorinated biphenyls in the Great Lakes[J]. AIMS Environmental Science, 2018, 5(1): 1-23. doi: 10.3934/environsci.2018.1.1

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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.

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