Risk assessment of Arctic aquatic species using ecotoxicological biomarkers and Bayesian network. (July 2020)

• Record Type: Journal Article
• Authors: Fahd, Faisal; Veitch, Brian; Khan, Faisal
• Is Part Of: Marine pollution bulletin. Volume 156(2020)
• Publication Date: 2020-07
• Subjects: Arctic, polar cod -- Boreogadus saida -- Ecotoxicity -- Bayesian network -- Toxicity mechanism; Marine pollution -- Periodicals; Marine Biology -- Periodicals; Water Pollution -- Periodicals; Mer -- Pollution -- Périodiques; Publications périodiques; Pollution des mers; Lutte antipollution; Electronic journals; 363.7394
• DOI: 10.1016/j.marpolbul.2020.111212
• Languages: English
• ISSNs: 0025-326X

Abstract

Abstract: The risk to Arctic aquatic species due to accidental oil spills is not well studied. One of the key reasons for this limitation is the lack of understanding of the dose-response relations for the species in the Arctic region. The present study addresses this knowledge gap. It proposes a new approach to develop dose-response curves for Arctic aquatic species. The application of the approach is demonstrated using the estimation of mortality risk in Boreogadus saida (polar cod) due to exposure from polycyclic aromatic hydrocarbons (PAH). The proposed approach considers the toxicity mechanism in Arctic species (i.e. polar cod) and regional environmental factors, and models these as a belief-based Bayesian Network (BN). The BN model integrates diverse ecotoxicology biomarker data types and predicts the cell death probability due to exposure to a toxicant (PAH in crude oil). The input data and results from the model were verified using data available in the literature. Seasonal sea ice played a major role in containing PAH exposure and subsequent risk to polar cod. However, the physiological factors, such as presence of higher Phase II activity, and higher oxyradical scavenging ability, had greater impact on PAH risk mitigation. Highlights: A new approach to develop dose-response curves for Arctic aquatic species is proposed. Model application is demonstrated using the estimation of polar cod mortality risk due to polycyclic aromatic hydrocarbons. A new Bayesian network model that integrates diverse ecotoxicology biomarker data and predicts the mortality is proposed. The model is tested for wide range of operating conditions.