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Quantifying carbon dioxide emission for Munich using a Bayesian inversion model

12.05.2021, Diplomarbeiten, Bachelor- und Masterarbeiten

The goal of the thesis is to provide a clearer insight of the carbon dioxide emissions in Munich through optimizing the emissions based on a Bayesian inversion model.

Overview:
The share of greenhouse gas (GHG) emissions released from urban areas has continued to increase as a result of urbanization. Carbon dioxide (CO2) emissions from energy use in cities are estimated to comprise more than 75 % of the global energy-related CO2. Thus, cities play a vital role in addressing the challenge of carbon mitigation. Munich, as the third largest city of Germany, sets an ambitious goal of carbon neutral by 2030. To achieve this target efficiently, the scientific findings related to carbon emissions in Munich are crucial and essential for developing effective and coherent adaptation actions. The Bayesian inversion approach is widely used for the quantification of carbon emissions. Specifically, it combines a transport model with a Bayesian inversion framework to estimate the surface emissions and their uncertainties.

The tasks will include:
1) Improvement of the inversion model framework;
2) Model running for 2019 and 2020;
3) Error analysis about the inversion model;
Your preferable skills and background are:
- Experience with R and Matlab
- Some basic knowledge about the Bayesian theory
- Motivation and interest in modeling atmospheric environment

For inquiries, please contact
Xinxu Zhao, xinxu.zhao@tum.de

Kontakt: xinxu.zhao@tum.de

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