Designing CO2 sensor networks for German cities: Insights from synthetic studies in Berlin and Munich

Cities are major contributors to global anthropogenic CO₂ emissions and their share relative to national emissions is increasing in many countries. This makes urban areas critical targets for effective CO₂ mitigation strategies. To monitor and verify mitigation efforts, measurement-based emission estimates of anthropogenic CO₂ emissions can be used. Many cities, however, lack the infrastructure to precisely constrain these emissions. 

To support the development of urban sensor networks within the Integrated Greenhouse Gas Monitoring System (ITMS) for Germany, we use Observing System Simulation Experiments (OSSEs). In these experiments, we evaluate synthetic in-situ sensor networks in Berlin and Munich with regards to their potential to constrain anthropogenic CO₂ emissions. 

Our OSSEs use a Bayesian inversion framework, with atmospheric transport simulated by the Lagrangian Particle Dispersion Model FLEXPART-WRF and meteorology computed by the Weather Research and Forecasting (WRF) model at a 1 km resolution for urban areas over an entire year. 

We analyze the effect of number, location and precision of CO₂ sensors, as well as of co-located CO concentration measurements.  We suggest favorable city-specific sensor network configurations and identify key factors for efficient network designs across the two cities. Our results support the deployment of efficient and effective sensor networks for measurement-based CO₂ emission monitoring and verification in Berlin, Munich and similar cities and will be the basis for future planned sensor network installations in Germany.