Building upon our previous research works, we have come to understand that German metropolitan areas do not exhibit improvements in air quality despite decreasing emissions. This is particularly evident concerning secondary air pollutants such as O₃ and PM_2.5. The primary factor behind this issue is the non-linear chemistry that interacts between primary emissions and the formation of secondary pollutants.
The primary objective of this project is to address a pivotal question: When and which emissions can be effectively reduced to genuinely enhance air quality in Munich? A collaborative effort between three distinguished groups - Technical University of Munich (Dr.-Ing. Jia Chen), Harvard University (Dr. Frank N. Keutsch), and Karlsruhe Institute of Technology (Dr. Harald Saathoff) - aims to comprehensively understand and improve air quality in Munich.
To address this complexity and pave the way for a cleaner future, the project focuses on gaining crucial insights into secondary pollutant formation and the role of precursors like NO_X and various VOCs. In 2023 (summer) and 2024 (winter), a planned campaign will measure secondary pollutants and their precursors through real-time measurements. This data, combined with advanced modeling tools, will enable the identification of suitable mitigation strategies.
Furthermore, the project will explore the potential of machine learning algorithms in modeling air pollutant concentrations¹. Additionally, there will be an effort to enhance the Chemical Transport Model (e.g., Geos-Chem) performance through the integration of machine learning models. Through this collaboration, the project aspires to contribute meaningful solutions that can benefit not only Munich but also inspire similar initiatives worldwide, ensuring a healthier and cleaner future for all. Funding for this significant endeavor is generously provided by the Institute for Advanced Studies (IAS), Technical University of Munich, under the prestigious Hans Fischer Senior Fellowship.

¹Balamurugan, V., Chen, J., Wenzel, A., and Keutsch, F. N.: Spatio-temporal modeling of air pollutant concentrations in Germany using machine learning, Atmos. Chem. Phys., 2023.