- 1Institut für Umweltphysik, Universität Heidelberg, Im Neuenheimer Feld 229, 69120 Heidelberg, Germany
- 2Heidelberg Institute for Geoinformation Technology, Berliner Strasse 45, 69120 Heidelberg
- 3Heidelberg Center for the Environment, Universität Heidelberg, Im Neuenheimer Feld 130.1, 69120 Heidelberg, Germany
- 4Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University, Heidelberg, 69120, Germany
Cities have a great responsibility to mitigate CO2 emissions, as they contribute substantially to global anthropogenic CO2. To assist cities in efficient mitigation planning an independent data-driven approach to monitor emissions within urban areas is required.
The Rhine-Neckar area comprises the cities of Mannheim and Heidelberg and is characterized by large emissions due to significant energy production and industry on the one hand, and by ambitious climate goals on the other hand. To monitor and support mitigation efforts of these cities, we are developing an urban monitoring network using mid-cost CO2 and air quality sensors for Heidelberg and Mannheim. The network will consist of 18 sensor nodes provided by the University of California, Berkeley. Each node is identical in construction to the sensors in the Berkeley Air Quality and CO2 Network (BEACO2N) (Shusterman et al., 2016) and measures CO2, CO, PM2.5 and NO2.
In conjunction with the measurement network, we use GRAMM/GRAL to model atmospheric transport in the domain on high resolution. GRAMM/GRAL is run following a catalog approach, in which hourly steady-state wind conditions are assumed. This way the computational costs can be reduced enabling the simulation of longer time scales on street canyon resolving spatial resolution (Berchet et al., 2017, May et al., 2024). We feed the model with high-resolution inventories of fossil fuel and biogenic emissions and compare the simulated enhancements to the measurements of the first deployed nodes. We discuss the capability of the conjunction of high-resolution modeling and mid-cost observations to detect emission patterns within the Rhine-Neckar area.
Shusterman, A. A., et al., (2016). The BErkeley Atmospheric CO2 Observation Network: initial evaluation. Atmos. Chem. Phys., 16, 13449–13463., https://doi.org/10.5194/acp-16-13449-2016
Berchet, A., et al., (2017). A cost-effective method for simulating city-wide air flow and pollutant dispersion at building resolving scale. Atmospheric Environment, 158, 181-196., https://doi.org/10.1016/j.atmosenv.2017.03.030
May, M., et al., (2024). Evaluation of the GRAMM/GRAL Model for High-Resolution Wind Fields in Heidelberg, Germany. Atmospheric Research, 300, 107207., https://doi.org/10.1016/j.atmosres.2023.107207
How to cite: Murai von Buenau, K., Haas, P., Maiwald, R., Ulrich, V., Block, S., Butz, A., and Vardag, S. N.: Towards an urban CO2 and air pollution network in Heidelberg-Mannheim, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10965, https://doi.org/10.5194/egusphere-egu25-10965, 2025.
