Statistical Methods of Environmental Science
Exam number: 6419
Semester: from 1st semester
Duration of the module: One semester
Form of the module (i.e. obligatory, elective etc.): Elective
Frequency of module offer: irregularly
Prerequisites: Binding registration required.
Applicability of module for other study programmes:
Obligatory or elective in other study programmes. For further information check regulations of the study programme.
Person responsible for module: Prof. Dr. Wolfgang Schmid
Name of the professor: Prof. Dr. Wolfgang Schmid
Language of teaching: English
ECTS-Credits (based on the workload): 6
Workload and its composition (self-study, contact time):
Contact time (Lecture, tutorial etc.): 45 h; self-study: 135 h
Contact hours (per week in semester): 4
Methods and duration of examination:
Successful preparation of a term paper as well as presentation of the results of work
Emphasis of the grade for the final grade: Please check regulations of the study programme
Aim of the module (expected learning outcomes and competencies to be acquired):
The subject of environmetrics is the statistical analysis of environmental processes. Environmetrics has close relationships with many other fields of science like natural sciences, engineering, medicine and economics. As environmental issues become more complex and environmental decision-making strives to be more precise, quantitative analysis becomes more important. New questions are requiring the development of new statistical methods and quantitative techniques to provide answers. The students should get familiar with statistical methods that are used to analyze environmental data and learn how these methods can be successfully applied to environmental data.
Contents of the module:
Introductory lectures are planned. Besides that, a short revision of multiple linear regression theory and both a theoretical and practical introduction to Geostatistics will be provided. Moreover, other important information concerning seminar papers and presentations will be mentioned during the first meeting.
Teaching and learning methods:
Literature (compulsory reading, recommended literature):
Abdel H. El-Shaarawi and Walter W. Piegorsch: Encyclopedia of Environmetrics. Wiley, Chichester, 2002.
Bruno F. and Cocchi D. (2002). A unified strategy for building simple air quality indices.Environmetrics, 13, 243-261.
Bruno. F. and Cocchi D. (2007). Recovering information from synthetic air quality indexes.Environmetrics, 18, 345-359.
O. Bodnar, M. Cameletti, A. Fassò, and W. Schmid (2008) Comparing air quality in Italy, Germany and Poland using BC indexes. Atmospheric Environment, 42, 8412-8421.
Aitana Lertxundi-Manterola and Marc Saez (2009). Modelling of nitrogen dioxide (NO2) and fine particle matter (PM10) air pollution in the metropolitan areas of Barcelona and Bilbao, Spain. Environmetrics, 20, 477-493.
Johannes Staehelin, Christian Keller, Wernera Stahel, Kurt Schläpfer, Urs Steinemann, Toni Bürgin and Stefan Schneider (1997). Modelling emission of road traffic from a tunnel study. Environmetrics, 8, 219-239.
Francis W. Zwiers and Hans von Storch (2004). On the role of statistics in climate research.International Journal of climatology, 24, 665-680.
Gabriela Beblo and Wolfgang Schmid: Modeling High Frequency Wind Speed Data. January 2011, Europa-Universität Viadrina: Discussion Papers.
C. A. Glasbey and D. J. Allcroft (2008) A spatiotemporal auto-regressive moving average model for solar radiation. Journal of the Royal Statistical Society, 57, 343-355.
Registration in Moodle Viadrina required.