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A Review on the Environmental Fate Models for Predicting the Distribution of Engineered Nanomaterials in Surface Waters
Exposure assessment is a key component in the risk assessment of engineered nanomaterials (ENMs). While direct and quantitative measurements of ENMs in complex environmental matrices remain challenging, environmental fate models (EFMs) can be used alternatively for estimating ENMs’ distributions in...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7349326/ https://www.ncbi.nlm.nih.gov/pubmed/32604975 http://dx.doi.org/10.3390/ijms21124554 |
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author | Suhendra, Edward Chang, Chih-Hua Hou, Wen-Che Hsieh, Yi-Chin |
author_facet | Suhendra, Edward Chang, Chih-Hua Hou, Wen-Che Hsieh, Yi-Chin |
author_sort | Suhendra, Edward |
collection | PubMed |
description | Exposure assessment is a key component in the risk assessment of engineered nanomaterials (ENMs). While direct and quantitative measurements of ENMs in complex environmental matrices remain challenging, environmental fate models (EFMs) can be used alternatively for estimating ENMs’ distributions in the environment. This review describes and assesses the development and capability of EFMs, focusing on surface waters. Our review finds that current engineered nanomaterial (ENM) exposure models can be largely classified into three types: material flow analysis models (MFAMs), multimedia compartmental models (MCMs), and spatial river/watershed models (SRWMs). MFAMs, which is already used to derive predicted environmental concentrations (PECs), can be used to estimate the releases of ENMs as inputs to EFMs. Both MCMs and SRWMs belong to EFMs. MCMs are spatially and/or temporally averaged models, which describe ENM fate processes as intermedia transfer of well-mixed environmental compartments. SRWMs are spatiotemporally resolved models, which consider the variability in watershed and/or stream hydrology, morphology, and sediment transport of river networks. As the foundation of EFMs, we also review the existing and emerging ENM fate processes and their inclusion in recent EFMs. We find that while ENM fate processes, such as heteroaggregation and dissolution, are commonly included in current EFMs, few models consider photoreaction and sulfidation, evaluation of the relative importance of fate processes, and the fate of weathered/transformed ENMs. We conclude the review by identifying the opportunities and challenges in using EFMs for ENMs. |
format | Online Article Text |
id | pubmed-7349326 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-73493262020-07-21 A Review on the Environmental Fate Models for Predicting the Distribution of Engineered Nanomaterials in Surface Waters Suhendra, Edward Chang, Chih-Hua Hou, Wen-Che Hsieh, Yi-Chin Int J Mol Sci Review Exposure assessment is a key component in the risk assessment of engineered nanomaterials (ENMs). While direct and quantitative measurements of ENMs in complex environmental matrices remain challenging, environmental fate models (EFMs) can be used alternatively for estimating ENMs’ distributions in the environment. This review describes and assesses the development and capability of EFMs, focusing on surface waters. Our review finds that current engineered nanomaterial (ENM) exposure models can be largely classified into three types: material flow analysis models (MFAMs), multimedia compartmental models (MCMs), and spatial river/watershed models (SRWMs). MFAMs, which is already used to derive predicted environmental concentrations (PECs), can be used to estimate the releases of ENMs as inputs to EFMs. Both MCMs and SRWMs belong to EFMs. MCMs are spatially and/or temporally averaged models, which describe ENM fate processes as intermedia transfer of well-mixed environmental compartments. SRWMs are spatiotemporally resolved models, which consider the variability in watershed and/or stream hydrology, morphology, and sediment transport of river networks. As the foundation of EFMs, we also review the existing and emerging ENM fate processes and their inclusion in recent EFMs. We find that while ENM fate processes, such as heteroaggregation and dissolution, are commonly included in current EFMs, few models consider photoreaction and sulfidation, evaluation of the relative importance of fate processes, and the fate of weathered/transformed ENMs. We conclude the review by identifying the opportunities and challenges in using EFMs for ENMs. MDPI 2020-06-26 /pmc/articles/PMC7349326/ /pubmed/32604975 http://dx.doi.org/10.3390/ijms21124554 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Review Suhendra, Edward Chang, Chih-Hua Hou, Wen-Che Hsieh, Yi-Chin A Review on the Environmental Fate Models for Predicting the Distribution of Engineered Nanomaterials in Surface Waters |
title | A Review on the Environmental Fate Models for Predicting the Distribution of Engineered Nanomaterials in Surface Waters |
title_full | A Review on the Environmental Fate Models for Predicting the Distribution of Engineered Nanomaterials in Surface Waters |
title_fullStr | A Review on the Environmental Fate Models for Predicting the Distribution of Engineered Nanomaterials in Surface Waters |
title_full_unstemmed | A Review on the Environmental Fate Models for Predicting the Distribution of Engineered Nanomaterials in Surface Waters |
title_short | A Review on the Environmental Fate Models for Predicting the Distribution of Engineered Nanomaterials in Surface Waters |
title_sort | review on the environmental fate models for predicting the distribution of engineered nanomaterials in surface waters |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7349326/ https://www.ncbi.nlm.nih.gov/pubmed/32604975 http://dx.doi.org/10.3390/ijms21124554 |
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