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Systematic Handling of Environmental Fate Data for Model Development─Illustrated for the Case of Biodegradation Half-Life Data


Hafner, Jasmin; Fenner, Kathrin; Scheidegger, Andreas (2023). Systematic Handling of Environmental Fate Data for Model Development─Illustrated for the Case of Biodegradation Half-Life Data. Environmental science & technology letters, 10(10):859-864.

Abstract

The assessment of environmental hazard indicators such as persistence, mobility, toxicity, or bioaccumulation of chemicals often results in highly variable experimental outcomes. Persistence is particularly affected due to a multitude of influencing environmental factors, with biodegradation experiments resulting in half-lives spanning several orders of magnitude. Also, half-lives may lie beyond the limits of reliable half-life quantification, and the number of available data points per substance may vary considerably, requiring a statistically robust approach for the characterization of data. Here, we apply Bayesian inference to address these challenges and characterize the distributions of reported soil half-lives. Our model estimates the mean, standard deviation, and corresponding uncertainties from a set of reported half-lives experimentally obtained for a single substance. We apply our inference model to 893 pesticides and pesticide transformation products with experimental soil half-lives of varying data quantity and quality, and we infer the half-life distribution for each compound. By estimating average half-lives, their experimental variability, and the uncertainty of the estimations, we provide a reliable data source for building predictive models, which are urgently needed by regulatory authorities to manage existing chemicals and by industry to design benign, nonpersistent chemicals. Our approach can be readily adapted for other environmental hazard indicators.

Abstract

The assessment of environmental hazard indicators such as persistence, mobility, toxicity, or bioaccumulation of chemicals often results in highly variable experimental outcomes. Persistence is particularly affected due to a multitude of influencing environmental factors, with biodegradation experiments resulting in half-lives spanning several orders of magnitude. Also, half-lives may lie beyond the limits of reliable half-life quantification, and the number of available data points per substance may vary considerably, requiring a statistically robust approach for the characterization of data. Here, we apply Bayesian inference to address these challenges and characterize the distributions of reported soil half-lives. Our model estimates the mean, standard deviation, and corresponding uncertainties from a set of reported half-lives experimentally obtained for a single substance. We apply our inference model to 893 pesticides and pesticide transformation products with experimental soil half-lives of varying data quantity and quality, and we infer the half-life distribution for each compound. By estimating average half-lives, their experimental variability, and the uncertainty of the estimations, we provide a reliable data source for building predictive models, which are urgently needed by regulatory authorities to manage existing chemicals and by industry to design benign, nonpersistent chemicals. Our approach can be readily adapted for other environmental hazard indicators.

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Additional indexing

Item Type:Journal Article, not_refereed, original work
Communities & Collections:07 Faculty of Science > Department of Chemistry
Dewey Decimal Classification:540 Chemistry
Scopus Subject Areas:Physical Sciences > Environmental Chemistry
Physical Sciences > Ecology
Physical Sciences > Water Science and Technology
Physical Sciences > Waste Management and Disposal
Physical Sciences > Pollution
Physical Sciences > Health, Toxicology and Mutagenesis
Uncontrolled Keywords:Health, Toxicology and Mutagenesis, Pollution, Waste Management and Disposal, Water Science and Technology, Ecology, Environmental Chemistry
Language:English
Date:10 October 2023
Deposited On:23 Feb 2024 09:43
Last Modified:30 Jun 2024 03:34
Publisher:American Chemical Society (ACS)
ISSN:2328-8930
OA Status:Hybrid
Publisher DOI:https://doi.org/10.1021/acs.estlett.3c00526
PubMed ID:37840818
Project Information:
  • : FunderH2020
  • : Grant ID875508
  • : Project TitlePREMIER - Prioritisation and Risk Evaluation of Medicines in the EnviRonment
  • Content: Published Version
  • Language: English
  • Licence: Creative Commons: Attribution 4.0 International (CC BY 4.0)