In ecotoxicological risk assessment, the estimation of a Species Sensitivity Distribution (SSD) is a routine method used to derive hazardous levels of concentrations for chemical substances. Here, we propose a Bayesian hierarchical approach leading to the definition of a new SSD. Our approach allows to use all information available at chemical-class-species levels to make inferential decisions. We estimate parameters via computer-intensive methods based on Markov Chain Monte Carlo methods, and we propose a way to derive the estimates of concern levels of toxicants that could be easily adopted in ecotoxicological risk management.

Migliorati, S., Monti, G. (2021). A Bayesian Mixture Model for Ecotoxicological Risk Assessment. In P. Mariani, M. Zenga (a cura di), Data Science and Social Research II. DSSR 2019. Studies in Classification, Data Analysis, and Knowledge Organization (pp. 281-291). Springer [10.1007/978-3-030-51222-4_22].

A Bayesian Mixture Model for Ecotoxicological Risk Assessment

Migliorati, S;Monti, GS
2021

Abstract

In ecotoxicological risk assessment, the estimation of a Species Sensitivity Distribution (SSD) is a routine method used to derive hazardous levels of concentrations for chemical substances. Here, we propose a Bayesian hierarchical approach leading to the definition of a new SSD. Our approach allows to use all information available at chemical-class-species levels to make inferential decisions. We estimate parameters via computer-intensive methods based on Markov Chain Monte Carlo methods, and we propose a way to derive the estimates of concern levels of toxicants that could be easily adopted in ecotoxicological risk management.
Capitolo o saggio
Species Sensitivity Distribution, MCMC
English
Data Science and Social Research II. DSSR 2019. Studies in Classification, Data Analysis, and Knowledge Organization
2021
978-3-030-51221-7
Migliorati, S., Monti, G. (2021). A Bayesian Mixture Model for Ecotoxicological Risk Assessment. In P. Mariani, M. Zenga (a cura di), Data Science and Social Research II. DSSR 2019. Studies in Classification, Data Analysis, and Knowledge Organization (pp. 281-291). Springer [10.1007/978-3-030-51222-4_22].
Migliorati, S; Monti, G
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/296306
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