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Application of new statistical distribution approaches for environmental mixture risk assessment: A case study
OBJECTIVES: There is growing evidence that single substances present below their individual thresholds of effect may still contribute to combined effects. In component-based mixture risk assessment (MRA), the risks can be addressed using information on the mixture components. This is, however, often...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6839615/ https://www.ncbi.nlm.nih.gov/pubmed/31357034 http://dx.doi.org/10.1016/j.scitotenv.2019.07.316 |
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author | Kienzler, Aude Bopp, Stephanie Halder, Marlies Embry, Michelle Worth, Andrew |
author_facet | Kienzler, Aude Bopp, Stephanie Halder, Marlies Embry, Michelle Worth, Andrew |
author_sort | Kienzler, Aude |
collection | PubMed |
description | OBJECTIVES: There is growing evidence that single substances present below their individual thresholds of effect may still contribute to combined effects. In component-based mixture risk assessment (MRA), the risks can be addressed using information on the mixture components. This is, however, often hampered by limited availability of ecotoxicity data. Here, the possible use of ecotoxicological threshold concentrations of no concern (i.e. 5th percentile of statistical distribution of ecotoxicological values) is investigated to fill data gaps in MRA. METHODS: For chemicals without available aquatic toxicity data, ecotoxicological threshold concentrations of no concern have been derived from Predicted No Effect Concentration (PNEC) distributions and from chemical toxicity distributions, using the EnviroTox tool, with and without considering the chemical mode of action. For exposure, chemical monitoring data from European rivers have been used to illustrate four realistic co-exposure scenarios. Based on those monitoring data and available ecotoxicity data or threshold concentrations when no data were available, Risk Quotients for individual chemicals were calculated, to then derive a mixture Risk Quotient (RQmix). RESULTS: A risk was identified in two of the four scenarios. Threshold concentrations contribute from 24 to 95% of the whole RQmix; thus they have a large impact on the predicted mixture risk. Therefore they could only be used for data gap filling for a limited number of chemicals in the mixture. The use of mode of action information to derive more specific threshold values could be a helpful refinement in some cases. |
format | Online Article Text |
id | pubmed-6839615 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-68396152019-11-25 Application of new statistical distribution approaches for environmental mixture risk assessment: A case study Kienzler, Aude Bopp, Stephanie Halder, Marlies Embry, Michelle Worth, Andrew Sci Total Environ Article OBJECTIVES: There is growing evidence that single substances present below their individual thresholds of effect may still contribute to combined effects. In component-based mixture risk assessment (MRA), the risks can be addressed using information on the mixture components. This is, however, often hampered by limited availability of ecotoxicity data. Here, the possible use of ecotoxicological threshold concentrations of no concern (i.e. 5th percentile of statistical distribution of ecotoxicological values) is investigated to fill data gaps in MRA. METHODS: For chemicals without available aquatic toxicity data, ecotoxicological threshold concentrations of no concern have been derived from Predicted No Effect Concentration (PNEC) distributions and from chemical toxicity distributions, using the EnviroTox tool, with and without considering the chemical mode of action. For exposure, chemical monitoring data from European rivers have been used to illustrate four realistic co-exposure scenarios. Based on those monitoring data and available ecotoxicity data or threshold concentrations when no data were available, Risk Quotients for individual chemicals were calculated, to then derive a mixture Risk Quotient (RQmix). RESULTS: A risk was identified in two of the four scenarios. Threshold concentrations contribute from 24 to 95% of the whole RQmix; thus they have a large impact on the predicted mixture risk. Therefore they could only be used for data gap filling for a limited number of chemicals in the mixture. The use of mode of action information to derive more specific threshold values could be a helpful refinement in some cases. Elsevier 2019-11-25 /pmc/articles/PMC6839615/ /pubmed/31357034 http://dx.doi.org/10.1016/j.scitotenv.2019.07.316 Text en © 2019 The Authors http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Kienzler, Aude Bopp, Stephanie Halder, Marlies Embry, Michelle Worth, Andrew Application of new statistical distribution approaches for environmental mixture risk assessment: A case study |
title | Application of new statistical distribution approaches for environmental mixture risk assessment: A case study |
title_full | Application of new statistical distribution approaches for environmental mixture risk assessment: A case study |
title_fullStr | Application of new statistical distribution approaches for environmental mixture risk assessment: A case study |
title_full_unstemmed | Application of new statistical distribution approaches for environmental mixture risk assessment: A case study |
title_short | Application of new statistical distribution approaches for environmental mixture risk assessment: A case study |
title_sort | application of new statistical distribution approaches for environmental mixture risk assessment: a case study |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6839615/ https://www.ncbi.nlm.nih.gov/pubmed/31357034 http://dx.doi.org/10.1016/j.scitotenv.2019.07.316 |
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