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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Table of Contents
1 Introduction
2 Evaluation of models
2.1 Assuring the quality of models
2.1.1 The validation problem
2.1.2 External and internal validation and software evaluation
2.1.3 The importance of the model’s purpose
2.2 Model validation methodology
2.2.1 Internal validation
2.2.2 External validation
2.2.3 Both aspects of validation
2.3 Software evaluation methodology
2.3.1 Quality testing of software
2.3.2 Quality requirements regarding ISO/IEC 12119
2.3.3 Quality requirements for risk assessment programmes
2.4 Discussion
2.5 Conclusions
2.6 Summary
3 Handling Uncertainties
3.1 Types of uncertainty
3.1.1 Uncertainties in exposure assessment
3.1.2 True parameter uncertainty and parameter variability
3.2 Sensitivity analyses
3.2.1 Background and benefit
3.2.2 Methodology
3.3 Scenario analyses
3.3.1 Point estimates
3.3.2 Limitations of the approach
3.4 Probabilistic analyses
3.4.1 Background
3.4.2 Methodological survey
3.4.3 Benefits
3.4.4 Monte-Carlo analyses
3.4.5 Probability distributions
3.5 Standards for exposure assessments
3.5.1 View on the US situation
3.5.2 View on the EU and German situation
3.6 Discussion and conclusions
3.6.1 Methodology for handling the different types of uncertainties
3.6.2 Sensitivity analysis methodology
3.6.3 Probabilistic analysis methodology
3.6.4 The methodology in the context of model validation
3.7 Summary
4 Exposure models
4.1 Terminology
4.2 Types of models
4.3 Description of the models’ structure and equations
4.3.1 Overall system
4.3.2 Fish
4.3.3 Meat and milk
4.3.4 Plants
4.3.5 Drinking water
4.3.6 Human exposure
4.4 Purpose of the models and software
4.5 Probabilistic extension of the models
4.6 Discussion and conclusions
4.7 Summary
5 Substances and parameters
5.1 Selected substances
5.1.1 Polychlorinated dibenzo-p-dioxins (PCDD)
5.1.2 Polychlorinated biphenyls (PCB)
5.1.3 Di-(2-ethylhexyhl)phthalate (DEHP)
5.1.4 Hexahydro-hexamethyl-cyclopenta-[g]-2-benzopyrane (HHCB)
5.1.5 Linear alkyl benzene sulfonates (LAS)
5.1.6 Ethylendiaminetetra acetic acid (EDTA)
5.1.7 1,2-Dichloroethane (EDC)
5.1.8 Benzene (BENZ)
5.2 Input parameters
5.2.1 Parameters for the regional distribution model and its respective scenarios
5.2.2 Parameters of the exposure module
5.2.3 Concentrations
5.3 Evaluative terms for the external validation
5.3.1 Accuracy and uncertainty in effect assessment
5.3.2 Definition of evaluative terms
5.4 Summary
6 Inspection of theory
6.1 Verification
6.2 Underlying assumptions
6.2.1 Fish
6.2.2 Meat and milk
6.2.3 Plants
6.2.4 Drinking water
6.2.5 Human exposure
6.3 Conclusions
6.4 Summary
7 Sensitivity analyses
7.1 Analytic approach
7.2 Substance-based approach (overall system)
7.3 Substance-based approach (exposure module only)
7.4 Conclusions
7.5 Summary
8 Scenario analyses and comparison with measured data
8.1 Bioconcentration model fish
8.1.1 Comparison with experimental data
8.1.2 Comparison to the monitoring data
8.2 Biotransfer into milk and meat
8.3 Uptake by plants
8.4 Human exposure
8.4.1 Predicted doses
8.4.2 Contribution of the exposure pathways
8.5 Concluding evaluation
8.6 Summary
9 Probabilistic uncertainty analyses
9.1 Uncertainty impact analyses of individual parameters
9.2 Cumulative distribution functions of the total daily dose
9.2.1 Comparison with point estimates
9.2.2 Comparison with alternative assessments
9.2.3 Impact of ignoring correlations
9.2.4 Impact of unknown degradation rates
9.2.5 Impact of other age-specific intake rates
9.3 Uncertainty impact analyses of parameter groups
9.4 Conclusions
9.5 Summary
10 Comparison with alternative models
10.1 Alternatives to the bioconcentration model for fish
10.2 Alternatives to the biotransfer model for meat and milk
10.3 Alternatives to the plant model
10.4 Alternative human exposure pathways
10.5 Conclusions
10.6 Summary
11 Software evaluation
11.1 Product description
11.2 Documentation
11.2.1 Printed documentation
11.2.2 Online documentation
11.3 Technical requirements
11.3.1 Installation and system requirements
11.3.2 Stability and reliability
11.3.3 State-of-the-art
11.3.4 Network-support
11.3.5 Miscellaneous
11.4 Correctness of calculations (verification)
11.5 User interface and operability
11.6 Transparency
11.7 Features
11.8 Cooperation with other programmes
11.9 Uncertainty analyses capability
11.10 Support
11.11 Conclusions and proposals
11.12 Summary
12 Conclusions
12.1 Applicability of the models
12.2 Database
12.3 Classes of chemicals posing problems
12.4 General remarks regarding applicability
12.5 Conceptual suggestions
12.6 Concluding remarks
Research Objectives and Themes
This doctoral thesis provides a comprehensive evaluation of the exposure models used within the European Union's risk assessment framework (TGD and EUSES). The study focuses on quantifying the reliability, applicability, and limitations of these models for predicting human chemical intake via the environment, specifically through food chains and other exposure pathways, while developing a structured methodology for model validation and software quality assurance.
- Evaluation of mathematical models for human exposure assessment.
- Development of a formal validation protocol for chemical risk assessment.
- Comprehensive sensitivity and uncertainty analysis of exposure models.
- Critical assessment of the EUSES software regarding transparency and technical accuracy.
- Application of the proposed methodology to a variety of representative chemical substances.
Excerpt from the Book
The validation problem
The construction and use of mathematical models for exposure assessment are crucial in the context of environmental risk assessment for chemical substances (LEEUWEN AND VAN HERMENS 1995). After the development (or synthesis) of a model, questions concerning its applicability emerge: is my model applicable to the class of chemicals under consideration? Can I justify a carry-over of the model from one chemical to another? How accurate are the predicted results? Does the conceptual structure of the model reflect that of the real phenomena? Given a certain task, is my model better than another one? To recapitulate: should I use the model?
In any case, a concept termed as validation (from validus (lat.)) is used to answer these questions. But in the scientific community the concept of validation is debatable, it is defined inconsistently and has led into an intellectual impasse (BECK AND CHEN 2000). Confusions arise from the philosophical question to what extent, if at all, models or more generally scientific theories can be validated. Not only commonly accepted fundamental works of POPPER (1963, 1959) show that the truth of a scientific theory cannot be proved, at best it can only be invalidated. Despite this, the public has its own understanding of what the word validation implies and is misled by this expression (BREDEHOEFT AND KONIKOW 1993). Even among modellers, who deem validation as a kind of confirmation, there is no clear and uniform concept and many expressions circulate. Confusion appears with such concepts as validation, verification, credibility, capability, adequacy, reliability, to name just a few. Despite their plethora and variety, all of these phrases emphasise the applicability of a model to perform a designated task. Against this background, papers have been written to place all encountered terms into an ordered context and to abolish the discords on validation (GAYLER 1999, BECK ET AL. 1997, RYKIEL 1995, ORESKES ET AL. 1994, SARGENT 1993). Nevertheless, the debate continues.
Summary of Chapters
1 Introduction: Introduces the framework of EU chemical risk assessment and the central goal of evaluating the TGD exposure models.
2 Evaluation of models: Establishes a formal methodology and protocol for validating exposure models and evaluating chemical assessment software.
3 Handling Uncertainties: Reviews theories of uncertainty and probability distribution selection in exposure assessments.
4 Exposure models: Details the structures, equations, and intended purpose of the human exposure models used in the TGD/EUSES framework.
5 Substances and parameters: Presents the set of reference substances and the database used for the validation and analysis in the study.
6 Inspection of theory: Performs a critical review of the theoretical assumptions and formal verification of the various submodels.
7 Sensitivity analyses: Identifies and ranks influential model parameters for different substance classes using an analytic approach.
8 Scenario analyses and comparison with measured data: Compares model-predicted values with real-world monitoring and experimental data for various chemicals.
9 Probabilistic uncertainty analyses: Provides a quantitative analysis of uncertainty propagation through the exposure models using probabilistic simulations.
10 Comparison with alternative models: Contrasts the TGD approach with more complex or alternative modelling methods found in current literature.
11 Software evaluation: Analyzes the EUSES software package against specific quality, technical, and transparency criteria.
12 Conclusions: Aggregates the study findings to assess model applicability, providing final recommendations for future improvements.
Keywords
Risk assessment, TGD, EUSES, quality assurance, model validation, software evaluation, fate and exposure models, uncertainty analysis, sensitivity analysis, scenario analysis, assumptions, limitations
Frequently Asked Questions
What is the core focus of this research?
The research evaluates the validity, applicability, and limitations of mathematical exposure models (TGD/EUSES) used in European Union risk assessment to predict chemical intake by humans.
Which key thematic areas are addressed?
The work addresses model validation, software evaluation, sensitivity and uncertainty analyses, and a comparison of model results against real-world field data.
What is the primary objective of this work?
The primary goal is to develop a robust methodology for validating exposure models and to identify under which conditions these models provide reliable predictions or, conversely, where they exhibit significant limitations.
Which scientific methods are utilized?
The study employs a combination of theoretical inspection of underlying assumptions, mathematical sensitivity analysis, scenario-based model comparison with empirical data, and probabilistic uncertainty analysis using Monte Carlo techniques.
What content does the main body of the work cover?
It includes a detailed examination of exposure modelling processes, an evaluation of the associated EUSES software, and a critical look at how chemical substances and environmental parameters impact model output accuracy.
What are the characterizing keywords of the study?
The study is characterized by terms such as risk assessment, EUSES, model validation, uncertainty analysis, sensitivity analysis, and environmental exposure modeling.
How does the work classify substances for risk assessment?
It classifies substances based on their physico-chemical properties (such as lipophilicity, volatility, and dissociation behavior), as these strongly influence the sensitivity and reliability of the models.
What is the conclusion regarding the software EUSES?
While EUSES is considered a modern and stable tool for risk assessment, the study highlights critical issues regarding its high complexity, lack of transparency, and poor modularity, leading to recommendations for future design improvements.
- Quote paper
- Stefan Schwartz (Author), 2000, Quality Assurance of Exposure Models for Environmental Risk Assessment of Substances, Munich, GRIN Verlag, https://www.grin.com/document/547
