Summary
Environmental risk assessment of chemical substances in the European Union is based on a harmonised scheme. The required models and parameters are laid down in the Technical Guidance Document (TGD) and are implemented in the EUSES software. Although the results may have a considerable ecological and economic impact, guidance is rarely given on the applicability of the framework. To fill this gap, an evaluation study of the TGD exposure models was carried out. In particular, the models for estimating chemical intake by humans were investigated. These models, which are a key component in risk assessment, involve a quantification of human contact with environmental contamination in various media of exposure through various exposure pathways. The objective of this study was two-fold: firstly, to develop an evaluation methodology, since no appropriate
approach is available in the scientific literature. Secondly, to elaborate applicability and limitations of the models and to provide proposals for their improvement.
The principles of model evaluation in terms of quality assurance, model validation and software evaluation were elaborated and a suitable evaluation protocol for chemical risk assessment models
was developed. Since scientific theories and the mathematical models embedded therein cannot be proved as true, a pragmatic meaning of validation is required, of which the primary purpose is to
increase the level of confidence placed in the model. The accuracy of the model outcome is a necessary, but insufficient criterion for the quality assurance of models. A wider approach is required
which examines the scientific inference that can be made about models with regard to their intended purpose. By reviewing the literature on the validation problem, it was found that all the facets of validation can be assigned to generic (internal) and task-specific (external) properties of a model. In this context, sensitivity and uncertainty analyses are essential to tackle the issues of uncertainty. Sensitivity analysis aims to ascertain how a given model depends upon the information fed into it. Uncertainty analysis aims to quantify the uncertainty regarding what comes out of the model. It was argued that targeted uncertainty analysis and sensitivity analysis, as a part of it, is capable of reducing critical uncertainties and represents an essential contribution for assuring the quality of a model.
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Inhaltsverzeichnis (Table of Contents)
- Introduction
- Evaluation of models
- Assuring the quality of models
- The validation problem
- External and internal validation and software evaluation
- The importance of the model's purpose
- Model validation methodology
- Internal validation
- External validation
- Both aspects of validation
- Software evaluation methodology
- Quality testing of software
- Quality requirements regarding ISO/IEC 12119
- Quality requirements for risk assessment programmes
- Discussion
- Conclusions
- Summary
- Assuring the quality of models
- Handling Uncertainties
- Types of uncertainty
- Uncertainties in exposure assessment
- True parameter uncertainty and parameter variability
- Sensitivity analyses
- Background and benefit
- Methodology
- Scenario analyses
- Point estimates
- Limitations of the approach
- Probabilistic analyses
- Background
- Methodological survey
- Benefits
- Monte-Carlo analyses
- Probability distributions
- Standards for exposure assessments
- View on the US situation
- View on the EU and German situation
- Discussion and conclusions
- Methodology for handling the different types of uncertainties
- Sensitivity analysis methodology
- Probabilistic analysis methodology
- The methodology in the context of model validation
- Summary
- Types of uncertainty
- Exposure models
- Terminology
- Types of models
- Description of the models' structure and equations
- Overall system
- Fish
- Meat and milk
- Plants
- Drinking water
- Human exposure
- Purpose of the models and software
- Probabilistic extension of the models
- Discussion and conclusions
- Summary
- Substances and parameters
- Selected substances
- Polychlorinated dibenzo-p-dioxins (PCDD)
- Polychlorinated biphenyls (PCB)
- Di-(2-ethylhexyhl)phthalate (DEHP)
- Hexahydro-hexamethyl-cyclopenta-[g]-2-benzopyrane (HHCB)
- Linear alkyl benzene sulfonates (LAS)
- Ethylendiaminetetra acetic acid (EDTA)
- 1,2-Dichloroethane (EDC)
- Benzene (BENZ)
- Input parameters
- Parameters for the regional distribution model and its respective scenarios
- Parameters of the exposure module
- Concentrations
- Evaluative terms for the external validation
- Accuracy and uncertainty in effect assessment
- Definition of evaluative terms
- Summary
- Selected substances
- Inspection of theory
- Verification
- Underlying assumptions
- Fish
- Meat and milk
- Plants
- Drinking water
- Human exposure
- Conclusions
- Summary
- Sensitivity analyses
- Analytic approach
- Substance-based approach (overall system)
- Substance-based approach (exposure module only)
- Conclusions
- Summary
- Scenario analyses and comparison with measured data
- Bioconcentration model fish
- Comparison with experimental data
- Comparison to the monitoring data
- Biotransfer into milk and meat
- Uptake by plants
- Human exposure
- Predicted doses
- Contribution of the exposure pathways
- Concluding evaluation
- Summary
- Bioconcentration model fish
- Probabilistic uncertainty analyses
- Uncertainty impact analyses of individual parameters
- Cumulative distribution functions of the total daily dose
- Comparison with point estimates
- Comparison with alternative assessments
- Impact of ignoring correlations
- Impact of unknown degradation rates
- Impact of other age-specific intake rates
- Uncertainty impact analyses of parameter groups
- Conclusions
- Summary
- Comparison with alternative models
- Alternatives to the bioconcentration model for fish
- Alternatives to the biotransfer model for meat and milk
- Alternatives to the plant model
- Alternative human exposure pathways
- Conclusions
- Summary
- Software evaluation
- Product description
- Documentation
- Printed documentation
- Online documentation
- Technical requirements
- Installation and system requirements
- Stability and reliability
- State-of-the-art
- Network-support
- Miscellaneous
- Correctness of calculations (verification)
- User interface and operability
- Transparency
- Features
- Cooperation with other programmes
- Uncertainty analyses capability
- Support
- Conclusions and proposals
- Summary
- Conclusions
- Applicability of the models
- Database
- Classes of chemicals posing problems
- General remarks regarding applicability
- Conceptual suggestions
- Concluding remarks
Zielsetzung und Themenschwerpunkte (Objectives and Key Themes)
The aim of this doctoral thesis is to evaluate the quality of exposure models used in environmental risk assessment. It examines how to ensure the quality of these models by addressing the validation process, specifically internal and external validation, and incorporating software evaluation. The thesis also explores different approaches to handling uncertainties in exposure assessment, including sensitivity analysis, scenario analysis, and probabilistic analysis. The work investigates the applicability of the models to various substances and parameters, considering real-world scenarios and comparing them with available data.
- Quality assurance of exposure models for environmental risk assessment.
- Validation methodologies for both internal and external validation.
- Handling uncertainties in exposure assessments through sensitivity analysis, scenario analysis, and probabilistic analysis.
- Applicability and limitations of exposure models for various substances and parameters.
- Comparison of model predictions with real-world data and alternative models.
Zusammenfassung der Kapitel (Chapter Summaries)
The thesis commences with a comprehensive introduction to the field of environmental risk assessment, specifically focusing on the importance of exposure models and their role in assessing the potential risks posed by substances. It outlines the challenges and objectives of the research project.
Chapter 2 delves into the crucial aspect of model evaluation. It presents a detailed examination of model validation, encompassing both internal and external validation methodologies, and explores the significance of the model's intended purpose. This chapter also includes a dedicated section on software evaluation, highlighting the critical importance of robust software for reliable risk assessment.
Chapter 3 shifts its focus to the complexities of handling uncertainties inherent in exposure assessment. It discusses various types of uncertainties, including parameter uncertainty and parameter variability, and presents different methodologies for addressing these uncertainties. The chapter examines sensitivity analyses, scenario analyses, and probabilistic analyses, providing a comprehensive overview of these crucial techniques.
Chapter 4 dives into the specifics of exposure models. It defines key terminology, categorizes different types of models, and provides a detailed description of their structure and equations. The chapter elaborates on the purpose and application of these models, along with a discussion of probabilistic extensions for enhancing their accuracy.
Chapter 5 focuses on the substances and parameters used in the exposure models. It presents a selection of substances commonly encountered in environmental risk assessment, including their properties and associated parameters. The chapter also includes a discussion of the evaluative terms used for external validation, ensuring a rigorous evaluation framework.
Chapter 6 examines the theoretical underpinnings of the exposure models. It focuses on the verification process, ensuring that the models are grounded in sound scientific principles. The chapter also meticulously explores the underlying assumptions that form the basis of these models, ensuring a thorough understanding of their theoretical framework.
Chapter 7 delves into the use of sensitivity analyses to assess the influence of various parameters on the model's predictions. It examines different approaches to sensitivity analyses, including an analytic approach and a substance-based approach, providing insights into the impact of parameter variability on model outcomes.
Chapter 8 investigates the application of scenario analyses to evaluate the performance of the exposure models in real-world scenarios. It compares the model predictions with measured data, examining specific examples of bioconcentration in fish, biotransfer into milk and meat, and uptake by plants. The chapter also explores the application of these models to human exposure, highlighting the importance of understanding the contribution of different exposure pathways.
Chapter 9 further explores the handling of uncertainties by employing probabilistic uncertainty analyses. It analyzes the impact of uncertainties in individual parameters and parameter groups, revealing the importance of accounting for variability in model inputs. This chapter also investigates the cumulative distribution functions of the total daily dose, providing a comprehensive picture of the potential range of exposure levels.
Chapter 10 delves into the comparison of the developed exposure models with alternative models commonly used in environmental risk assessment. It examines alternative models for bioconcentration in fish, biotransfer into milk and meat, and uptake by plants. The chapter also considers alternative human exposure pathways, offering a comprehensive perspective on the strengths and weaknesses of different modeling approaches.
Chapter 11 focuses on the evaluation of the software used for implementing the exposure models. It addresses aspects such as product description, documentation, technical requirements, correctness of calculations, user interface, and transparency. The chapter also examines the software's ability to perform uncertainty analyses, its features, and its compatibility with other software programs.
Schlüsselwörter (Keywords)
Exposure models, environmental risk assessment, validation, software evaluation, uncertainties, sensitivity analysis, scenario analysis, probabilistic analysis, substance parameters, human exposure, model applicability, model comparison, software evaluation, environmental protection.
- Citation du texte
- Stefan Schwartz (Auteur), 2000, Quality Assurance of Exposure Models for Environmental Risk Assessment of Substances, Munich, GRIN Verlag, https://www.grin.com/document/547
