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To support the European Green Deal and to assist cities in reaching net-zero emissions, we have developed an urban CO2 emission monitoring system combining a mesoscale atmospheric transport and inversion model with measurements from dense sensor networks. We have set up such a system for the city of Zurich, which includes a comprehensive measurement network and intensive campaigns conducted in the framework of the ICOS Cities project to provide a rich dataset for data assimilation and model validation. The network includes low- and mid-cost CO2 sensors and a tall flux tower. For CO2 data assimilation, we primarily use observations from the 21 mid-cost sensors, in particular the 14 sensors installed on rooftops, as they are easier for the model to reproduce. Additionally, we used measurements from three background sites located outside the city, as well as wind speed and temperature observations from meteorological sensors installed at most of the rooftop sensor locations. The atmospheric transport model ICON-ART was set up at a high resolution of about 600 m to resolve the complex topography of the area. The model domain extends about 60 km in the north-south and east-west directions, encompassing the city and all background stations. CO2 background concentrations at the domain boundaries were taken from a separate European-wide simulation, which itself was nested into global inversion-optimized CO2 simulations from the Copernicus Atmospheric Monitoring Service (CAMS). Prior anthropogenic emissions were based on the TNO-GHGco inventory for the European domain and on a composite of three inventories of increasing detail for the high-resolution domain, TNO-GHGco, a Swiss national inventory, and a Zurich city inventory. Another important source and sink of CO2 is the exchange with vegetation, which was calculated online in ICON-ART using the Vegetation Photosynthesis and Respiration Model (VPRM). Based on comparisons with observations, we continuously improved the forward simulations by introducing high-quality land-cover data, emissions from human respiration, and temporal profiles for the heating sector accounting for daily temperatures. Anthropogenic emissions and biospheric fluxes (respiration and gross photosynthetic production separately) are inversely estimated by coupling ICON-ART with the CarbonTracker Data Assimilation Shell (CTDAS), which employs an ensemble Kalman smoother to optimize a large number of flux scaling factors. Here we present our initial inversion experiments with both synthetic and real observations. The idealized setup with synthetically generated observations was used to optimize the system before applying it to real observations. Fluxes were estimated on a weekly scale at a grid cell level for multiple months between July 2022 and July 2023. The simulations show generally good agreement with the observations, but estimating anthropogenic emissions is challenging due to uncertainties in the biospheric fluxes and background CO2 concentrations. In its current state of development, the combination of measurements and the model allow reliable emission estimations mainly in winter when the regional anthropogenic CO2 signal is at its highest (20 50 ppm) and the biospheric signal is at its lowest. Acknowledgements: ICOS-Cities/PAUL, has received funding from the European Union's H2020 Programme under grant agreement No. 101037319
Ponomarev et al. (Fri,) studied this question.