Abstract: As more than 70% of fossil fuel-based carbon dioxide (CO2) is emitted in urban areas, urban greenhouse gas (GHG) emissions play a crucial role in achieving the emission reduction goals. In addition, air pollutants, such as nitrogen oxides (NOX), and particulate matter (PM) adversely affect urban air quality and are harmful to human health. In this talk, I will present new observational methods and modelling approaches to address two of the most urgent challenges of our time: climate change and air pollution.

I will present MUCCnet, the unique urban GHG monitoring sensor network (Dietrich et al. 2021). By combining the measured data with newly developed modelling methods based on computational fluid dynamics (Toja-Silva et al. 2017), Bayesian inversion (Jones et al. 2021), and machine learning (Gensheimer et al. 2022), we have been able to monitor the GHG emissions and sinks, and reveal unknown emission sources (Chen et al. 2020, Forstmaier et al. 2023) at different spatial scales. Furthermore, I will talk about our air quality network, consisting of 50 self-developed sensor systems and our study on the response of air pollutants to emission changes in German cities (Balamurugan et al. 2021).