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Content archived on 2024-05-21

Estimation of human IMPACT in the presence of natural fluctuations


The project aims at developing, testing and disseminating generic decision support tools that can match technological advancements in environmental monitoring and facilitate interpretation of time series environmental data. The approach is novel in the sense that mechanistic models based on physical, chemical and biological concepts and theories are fully integrated with statistical methods.

The objective of the project is to introduce a new set of tools which will bridge the present gap between purely statistical analysis of environmental monitoring data and mechanistic environmental modelling based on biological, chemical and physical concepts and theories. The specific tools objectives are to:
Match technological advances in environmental monitoring;
Facilitate estimation of environmental impact in the presence of natural fluctuations in the environment;
Bridge the gap between statistical data analysis and mechanistic modelling.
Moreover these tools also aim to involve the following specific procedures:
Time series decomposition methods in which a given series of environmental data is divided into two components respectively representing meteorologically-induced fluctuations and meteorologically normalised estimates of human impact;
Significance tests that permit retrospective impact assessment in the presence of co-variates representing natural fluctuations;
Model reduction procedures that facilitate merging of statistical and mechanistic approaches.

Work description:
Tools development facilitating interpretation of time series of environmental data is divided into three types of activities:
Development of generic computational procedures that can bridge the gap between statistical analysis of environmental monitoring data and mechanistic modelling based on physical, chemical and biological concepts and theories;
Testing of the procedures developed on a representative selection of environmental quality data and mechanistic models driven by meteorological or other naturally fluctuating inputs;
Incorporation of end-user views in the design of the decision support tools that shall be developed.

Scientific documentation of the methods and theoretical framework in which mechanistic models can be merged with statistical techniques to extract anthropogenic signals from time series of environmental data;
Scientific documentation of case studies on water and air quality that demonstrate the performance and benefits of the new tools compared to the tools presently used;
End-user-tested software and targeted presentations.


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Participants (7)