Aquatic Environmental Analysis
Syllabus, Bachelor's level, 1TV021
- Code
- 1TV021
- Education cycle
- First cycle
- Main field(s) of study and in-depth level
- Biology G1F, Earth Science G1F, Technology G1F
- Grading system
- Fail (U), Pass (3), Pass with credit (4), Pass with distinction (5)
- Finalised by
- The Faculty Board of Science and Technology, 18 March 2008
- Responsible department
- Department of Earth Sciences
Entry requirements
Biology, Ecology for engineers and Probability and Statistics.
Learning outcomes
After completion of the course the student should be able to:
- Apply an effect-load-sensitivity analysis to study effects from environmental problems in aquatic ecosystems
- Account for the most important environmental problems that affect society and aquatic ecosystems in Sweden and its immediate vicinity
- Rank these problems quantitatively
- Analyse environmental problems with a large areal distribution by means of geographical information systems
- Master basic principles for modelling expected results from measures against problems in the aquatic environment
Content
The part WATER POLLUTION treats the major chemical threats to mainly aquatic ecosystems, i. e., acidification (of mainly lakes, rivers and ground water), eutrophication (of mainly lakes and coastal areas) and toxic contamination (of metals, radionucleis and organic pollutants in mainly lakes, rivers and coastal areas). A system to rank chemical threats is also treated, as well as basic elements of ecosystem effect-load-senitivity analysis. The theoretical part of ECOSYSTEM MODELLING
focuses on basic structural components to mathematically describe the processes regulation spread, biouptake and effects of environmental toxins in ecosystems. The course focuses on basic principles for statistical/empirical models of environmental data (regression, transformations, etc.), basic different components of dynamical (i.e., time-dependent) models for environmental pollutants, and different methods to develop "mixed" statistical and dynamical models. An important aim of the course is to give insights into building and testing of predictive ecosystem models.
Instruction
Lectures and exercises.
Assessment
Written examination is held at the end of the course. Grading is on the scale 3, 4 or 5. A student who fails the examination can be examined again either at the beginning of the autumn or the spring term.
Reading list
- Reading list valid from Spring 2019
- Reading list valid from Autumn 2017, version 2
- Reading list valid from Autumn 2017, version 1
- Reading list valid from Autumn 2015
- Reading list valid from Autumn 2012
- Reading list valid from Autumn 2010
- Reading list valid from Autumn 2008
- Reading list valid from Spring 2005