Mathematics-driven environmental sensing (MES)

Human activities are significantly altering the atmosphere and threatening climate stability. In 2015, the Paris Agreement aimed to limit global warming to below 2°C by reducing greenhouse gas emissions. To avoid the catastrophic effects of climate change, it is crucial to identify and localize the sources of urban greenhouse gas emissions, as cities are major centers for human activities and have multiple overlapping sources of emissions. The TUM Professorship of Environmental Sensing and Modeling, led by PI Jia Chen, collaborates with the Professorship for Optimization and Data Analysis, headed by PI Felix Krahmer, to advance the understanding and methodologies of greenhouse gas emission assessment in urban environments. Using the measurements obtained by the Munich Urban Carbon Column network (MUCCnet) and a range of in-situ mid-cost and low-cost sensors across Munich, this project aims to apply fundamental mathematical methods and adapt compressed sensing techniques tailored to the challenges posed by environmental sensing data. Key milestones include overcoming limitations caused by high coherence in measurement matrices through irregular data discretization and quantifying spatial and statistical uncertainties in the localization process. By integrating mathematical theory and methods, this interdisciplinary effort strives to enhance the accuracy and reliability of environmental monitoring, ultimately contributing to more effective climate change mitigation strategies.