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Illustrating a Species Sensitivity Distribution for Nano‐ and Microplastic Particles Using Bayesian Hierarchical Modeling

Environmental contamination with nano‐ and microplastic (NMP) particles is an emerging global concern. The derivation of species sensitivity distributions (SSDs) is an essential step in estimating a hazardous concentration for 5% of the species (HC5), and this HC5 value is often used as a “safe” con...

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Autores principales: Takeshita, Kazutaka M., Iwasaki, Yuichi, Sinclair, Thomas M., Hayashi, Takehiko I., Naito, Wataru
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9314701/
https://www.ncbi.nlm.nih.gov/pubmed/35226391
http://dx.doi.org/10.1002/etc.5295
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author Takeshita, Kazutaka M.
Iwasaki, Yuichi
Sinclair, Thomas M.
Hayashi, Takehiko I.
Naito, Wataru
author_facet Takeshita, Kazutaka M.
Iwasaki, Yuichi
Sinclair, Thomas M.
Hayashi, Takehiko I.
Naito, Wataru
author_sort Takeshita, Kazutaka M.
collection PubMed
description Environmental contamination with nano‐ and microplastic (NMP) particles is an emerging global concern. The derivation of species sensitivity distributions (SSDs) is an essential step in estimating a hazardous concentration for 5% of the species (HC5), and this HC5 value is often used as a “safe” concentration in ecological risk assessment, that is, predicted‐no‐effect concentration. Although properties of plastics such as particle size can affect toxic effect concentrations, such influences have not yet been quantitatively considered in estimating SSDs for NMP particles. We illustrate a log‐normal SSD using chronic lowest‐observed‐effect concentrations (LOECs) of NMP particles from readily available toxicity data sets, considering the influence of particle size, polymer type, and freshwater or marine test media by adopting Bayesian hierarchical modeling techniques. Results of the hierarchical SSD modeling suggest that the SSD mean was negatively associated with particle size and was lower in marine media than in freshwater media. The posterior medians of the HC5 estimated from the LOEC‐based SSD varied by a factor of 10 depending on these factors (e.g., 1.8–20 μg/L for the particle size range of 0.1–5000 μm in the marine environment). Hierarchical SSD modeling allows us to clarify the influences of important factors such as NMP properties on effect concentrations, thereby helping to guide more relevant ecological risk assessments for NMP. Environ Toxicol Chem 2022;41:954–960. © 2022 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.
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spelling pubmed-93147012022-07-30 Illustrating a Species Sensitivity Distribution for Nano‐ and Microplastic Particles Using Bayesian Hierarchical Modeling Takeshita, Kazutaka M. Iwasaki, Yuichi Sinclair, Thomas M. Hayashi, Takehiko I. Naito, Wataru Environ Toxicol Chem Special Section Environmental contamination with nano‐ and microplastic (NMP) particles is an emerging global concern. The derivation of species sensitivity distributions (SSDs) is an essential step in estimating a hazardous concentration for 5% of the species (HC5), and this HC5 value is often used as a “safe” concentration in ecological risk assessment, that is, predicted‐no‐effect concentration. Although properties of plastics such as particle size can affect toxic effect concentrations, such influences have not yet been quantitatively considered in estimating SSDs for NMP particles. We illustrate a log‐normal SSD using chronic lowest‐observed‐effect concentrations (LOECs) of NMP particles from readily available toxicity data sets, considering the influence of particle size, polymer type, and freshwater or marine test media by adopting Bayesian hierarchical modeling techniques. Results of the hierarchical SSD modeling suggest that the SSD mean was negatively associated with particle size and was lower in marine media than in freshwater media. The posterior medians of the HC5 estimated from the LOEC‐based SSD varied by a factor of 10 depending on these factors (e.g., 1.8–20 μg/L for the particle size range of 0.1–5000 μm in the marine environment). Hierarchical SSD modeling allows us to clarify the influences of important factors such as NMP properties on effect concentrations, thereby helping to guide more relevant ecological risk assessments for NMP. Environ Toxicol Chem 2022;41:954–960. © 2022 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC. John Wiley and Sons Inc. 2022-02-28 2022-04 /pmc/articles/PMC9314701/ /pubmed/35226391 http://dx.doi.org/10.1002/etc.5295 Text en © 2022 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Special Section
Takeshita, Kazutaka M.
Iwasaki, Yuichi
Sinclair, Thomas M.
Hayashi, Takehiko I.
Naito, Wataru
Illustrating a Species Sensitivity Distribution for Nano‐ and Microplastic Particles Using Bayesian Hierarchical Modeling
title Illustrating a Species Sensitivity Distribution for Nano‐ and Microplastic Particles Using Bayesian Hierarchical Modeling
title_full Illustrating a Species Sensitivity Distribution for Nano‐ and Microplastic Particles Using Bayesian Hierarchical Modeling
title_fullStr Illustrating a Species Sensitivity Distribution for Nano‐ and Microplastic Particles Using Bayesian Hierarchical Modeling
title_full_unstemmed Illustrating a Species Sensitivity Distribution for Nano‐ and Microplastic Particles Using Bayesian Hierarchical Modeling
title_short Illustrating a Species Sensitivity Distribution for Nano‐ and Microplastic Particles Using Bayesian Hierarchical Modeling
title_sort illustrating a species sensitivity distribution for nano‐ and microplastic particles using bayesian hierarchical modeling
topic Special Section
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9314701/
https://www.ncbi.nlm.nih.gov/pubmed/35226391
http://dx.doi.org/10.1002/etc.5295
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