Modern Geochemistry utilizes three powerful tools: (major and trace) elements, isotopes and equations, to study various Earth and environmental processes. A combination of the experimental tools (elements and isotopes) with theoretical tools (equations) provides penetrating insights into the variability of natural processes.
1) Modern thermodynamics. From Heat Engines to Dissipative Structures. D. Kondepudi, I. Prigogine, Wiley, 1999.
2) Environmental Applications of Geochemical Modeling. C. Zhu, G. Anderson, Cambridge, 2002.
3) Geochemical and Biogeochemical Reaction Modeling. C. M. Bethke, Cambridge, 2008 (II edition).
4) An introduction to Applied Geostatistics. E.H. Isaaks, R.M.
5) Metodi matematici e statistici nelle Scienze della Terra vol. III, Tecniche statistiche. A. Buccianti, F. Rosso, F. Vlacci, Liguori Editore, 2003.
Learning Objectives
The student learns how to analyze experimental data obtained by geochemical surveys with the aim to model (from a thermodynamic and or statistical and geostatistical point of view) the variability characterizing natural processes.
Prerequisites
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Teaching Methods
Lectures, exercises in computer room or by using own laptop
Further information
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Type of Assessment
Final oral exam.
Course program
Environmental problems and the need for geochemical modeling. Thermodynamic background. Real and Model Systems. Thermodynamics for equilibrium conditions. Fluctuations and stability. Thermodynamics for non-equilibrium conditions: the linear regime. Order through fluctuations. Non-linear thermodynamics. Dissipative structures. Types of geochemical models (speciation-solubility, surface adsorption, reaction path, inverse mass balance, coupled mass transport, kinetics). Model verification and validation, usefulness and limitations. Computer programs for geochemical modeling.
Statistical and geostatistical background. Exploratory univariate and bivariate analysis. Deterministic an probabilistic models. Modeling regionalized and coregionalized phenomena. Mapping spatial data, problems and perspectives. Defining background and threshold, identification of data outliers and element sources.
Link between the thermo- dynamical and statistical approaches, self-organized geochemical systems for different scales, presence of fractals and multifractals structures.
Problems of compositional data analysis and nature (geometry) of the sample space.