E4220 Modelling and Interpretation of Environmetal Data

Faculty of Science
Spring 2022
Extent and Intensity
2/0/0. 2 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
Teacher(s)
Mgr. Jiří Komprda, Ph.D. (lecturer)
prof. Martin Scheringer, Dr. sc. nat. (lecturer)
Mgr. Klára Komprdová, Ph.D. (lecturer)
Guaranteed by
prof. Martin Scheringer, Dr. sc. nat.
RECETOX - Faculty of Science
Contact Person: Mgr. Jiří Komprda, Ph.D.
Supplier department: RECETOX - Faculty of Science
Prerequisites
NOW ( E4221 Model Interpret Env Data - Pr )
Elementary knowledge of environmental and physical chemistry, basics of differential and integral calculus of one variable, basic and multivariate statistics
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
Course objectives
The aim of the course is to present to students the contemporary level of mass-balance modelling of the environmental transport, fate and exposure of persistent pollutants and spatial and temporal statistical modelling. All environmental matrices are considered (air, water, sediments, soil and biota). During the course, several quantitative tools for describing, understanding and predicting the pollutants pathways are introduced. Practical training with the presented models is available within the associated seminar E4221
Learning outcomes
In the end of the course student should be able to understand and develop dynamic mass-balance and statistical models for environmental pollutants and utilize them in his/her research work; understand the accuracy, validity, and sensitivity of a chemical fate and transport model; understand the relationship between the chemical properties and environmental fate of organic chemicals.
Syllabus
  • 1) Introduction to environmental modeling; partitioning of chemicals in the environment part I 2) Partitioning of chemicals in the environment part II and the fugacity approach 3) Box models at different levels from closed system at equilibrium to open dynamic models 4) Degradation processes in the environment 5) Atmospheric fate and transport 6) Long-range transport and global distribution 7) Bioaccumulation 8) Experimental design for statistical models 9) Temporal and spatial modeling - Introduction 10) Time series models 11) Spatial interpolation models 12) Multivariate methods for identification and classification of pollution 13) Uncertainty and Sensitivity Analysis
Literature
    recommended literature
  • SCHERINGER, Martin. Persistence and spatial range of environmental chemicals: New ethical and scientific concepts for risk assessment. First edition. Wiley-VCH, 2002. 294 s. ISBN 9783527305278
  • SCHWARZENBACH, René P., P. M. GSCHWEND and Dieter M. IMBODEN. Environmental organic chemistry. Third edition. Hoboken, New Jersey: Wiley, 2016. xvii, 1005. ISBN 9781118767238. info
  • MACKAY, Donald. Multimedia environmental models : the fugacity approach. 2nd ed. Boca Raton, Fla.: Taylor & Francis, 2001. 261 s. ISBN 1566705428. info
    not specified
  • HENGL, T. A Practical Guide to Geostatistical Mapping of Environmental Variables. Luxemburg: EUR 22904 EN Scientific and Technical Research series, Office for Official Publications of the European Communities, 2007. 143 pp. ISBN 978-92-79-0690. info
  • HASTIE, Trevor, Robert TIBSHIRANI and J. H. FRIEDMAN. The elements of statistical learning : data mining, inference, and prediction. Corrected ed. New York: Springer, 2003. xvi, 533. ISBN 0387952845. info
Teaching methods
Theoretical lectures with illustrative examples of the models.
Assessment methods
The final exam consisting of a written test. Results of the homeworks from seminar E4221 are included in the overall evaluation.
Language of instruction
English
Further Comments
The course is taught annually.
The course is taught: every week.
Listed among pre-requisites of other courses
The course is also listed under the following terms Spring 2021.
  • Enrolment Statistics (recent)
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