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Predictive risk thresholds for survival protection of farmed abalone, Haliotis diversicolor supertexta, exposed to waterborne zinc
10.1002/tox.20096.abs
Using a probabilistic risk‐based framework, we have developed a simple predictive risk threshold model for protecting the survival of farmed abalone, Haliotis diversicolor supertexta, exposed to waterborne zinc (Zn). Probabilistic techniques using Monte Carlo analysis propagate parameter uncertainty/variability throughout the model, providing decision makers with a credible range of information and increased flexibility in establishing a specific Zn level in aquacultural ecosystems. We coupled a first‐order two‐compartment bioaccumulation model with a reconstructed dose–response profile based on a three‐parameter Hill equation model to form a probabilistic risk model in order to determine the risk quotient associated with a 10% probability of exceeding the abalone 5% effect concentration (EC5) at site‐specific abalone farms. Sensitivity analysis revealed that waterborne Zn concentration (Cw) and algae bioconcentration factor (BCFa) have a significant effect on Zn levels in abalone. Using multiple nonlinear regression analysis with Cw and BCFa as the parameters, a predictive risk threshold equation that can be used in a variety of site‐specific conditions was developed for protecting the survival of farmed abalone. We believe this probabilistic framework provides an effective method for conceptualizing a public policy decision vis‐à‐vis the establishment of a specific acceptable risk threshold for aquacultural water quality management. © 2005 Wiley Periodicals, Inc. Environ Toxicol 20: 202–211, 2005.
Predictive risk thresholds for survival protection of farmed abalone, Haliotis diversicolor supertexta, exposed to waterborne zinc
10.1002/tox.20096.abs
Using a probabilistic risk‐based framework, we have developed a simple predictive risk threshold model for protecting the survival of farmed abalone, Haliotis diversicolor supertexta, exposed to waterborne zinc (Zn). Probabilistic techniques using Monte Carlo analysis propagate parameter uncertainty/variability throughout the model, providing decision makers with a credible range of information and increased flexibility in establishing a specific Zn level in aquacultural ecosystems. We coupled a first‐order two‐compartment bioaccumulation model with a reconstructed dose–response profile based on a three‐parameter Hill equation model to form a probabilistic risk model in order to determine the risk quotient associated with a 10% probability of exceeding the abalone 5% effect concentration (EC5) at site‐specific abalone farms. Sensitivity analysis revealed that waterborne Zn concentration (Cw) and algae bioconcentration factor (BCFa) have a significant effect on Zn levels in abalone. Using multiple nonlinear regression analysis with Cw and BCFa as the parameters, a predictive risk threshold equation that can be used in a variety of site‐specific conditions was developed for protecting the survival of farmed abalone. We believe this probabilistic framework provides an effective method for conceptualizing a public policy decision vis‐à‐vis the establishment of a specific acceptable risk threshold for aquacultural water quality management. © 2005 Wiley Periodicals, Inc. Environ Toxicol 20: 202–211, 2005.
Predictive risk thresholds for survival protection of farmed abalone, Haliotis diversicolor supertexta, exposed to waterborne zinc
Liao, Chung‐Min (author) / Chou, Berry Yun‐Hua (author)
Environmental Toxicology ; 20 ; 202-211
2005-04-01
10 pages
Article (Journal)
Electronic Resource
English
abalone , risk , probabilistic , toxicity threshold , zinc
Probabilistic risk assessment of abalone Haliotis diversicolor supertexta exposed to waterborne zinc
| Online Contents | 2004