Quality Assurance of Exposure Models for Environmental Risk Assessment of Substances

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

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.