Spatial Aspects of Chemical Exposure Assessment: A Tool for River Networks.
Chemical exposure assessment has gained increasing attention in recent years. Its methodologies have enabled scientists and policy-makers to understand exposure paths and to identify environmental compartments of concern. Mathematical models are used for the prediction of a chemical's concentration in a certain compartment and in some cases also for predicting the duration or time of highest load. With the Geo-referenced Regional Exposure Assessment Tool for European Rivers (GREAT-ER) spatial aspects of regional exposure assessment are addressed for the "down-the-drain" path of consumer chemicals such as detergents.
On the basis of a carefully developed simulation model (Boeije, 1999), this thesis describes the concept and realization of the developed software tool GREAT-ER. With data composition and processing on the one hand and application and analysis on the other hand, two crucial aspects in spatial exposure assessment are identified and discussed.
Geo-referenced real-world data are not readily available in a usable form. An intermediate format is defined to separate the tasks of an initial preparation of raw data from the final aggregation leading to a directly usable data set. It is shown that the latter step can be fully automated and thus efficiently supports an iterative procedure of data quality improvement.
The application of GREAT-ER to the substances LAS (readily degradable) and boron (inert) in four Yorkshire catchments demonstrates the ability to predict mean final effluent and in-stream concentrations with an average error of less than a factor of 2. Furthermore, regional summaries and risk characterization add useful information to judging a regional response to the (potential) release of a substance.
In conclusion, the development and application of GREAT-ER has proven that geo-referenced exposure assessment is possible with regard to both quality and practicability. Future activities should focus on gaining further experiences in performing simulations, improving the tool itself and extending its abilities. Finally the integration of further models should be evaluated.
| Identifer | oai:union.ndltd.org:uni-osnabrueck.de/oai:repositorium.ub.uni-osnabrueck.de:urn:nbn:de:gbv:700-2001103115 |
| Date | 31 October 2001 |
| Creators | Wagner, Jan-Oliver |
| Contributors | Prof. Dr. Michael Matthies, Prof. dr. ir. Peter A. Vanrolleghem |
| Source Sets | Universität Osnabrück |
| Language | English |
| Detected Language | English |
| Type | doc-type:doctoralThesis |
| Format | application/gzip, application/pdf |
| Rights | http://rightsstatements.org/vocab/InC/1.0/ |
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