the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Monitoring CO2 in diverse European cities: Highlighting needs and challenges through characterisation
Abstract. For the development of a joint European capacity for monitoring CO2 emissions, we created the framework "CO2 Monitoring Challenges City Mapbooks v1.0" (acronym CMC-CITYMAP). It includes a Jupyter notebook tool (Storm et al., 2025a, https://doi.org/10.18160/P8SV-B99F) which we use to characterise and cluster cities based on aspects relevant for different CO2 monitoring challenges, including (a) determining background levels of CO2 inflow into a city ("background challenge"), (b) separating the anthropogenic emissions from the influence of the biosphere ("biogenic challenge"), (c) representing spatially and temporally non-uniform emissions in models ("modelling challenge"), and (d) implementing observation strategies not covered by the other challenges ("application-specific observational challenge"). We provide and discuss the challenges city-by-city basis, but our primary focus is on the relationships between cities: best practices and lessons learned from monitoring CO2 emissions in one city can be transferred to other cities with similar characteristics. Additionally, we identify cities with characteristics that strongly contrast with those of cities with existing urban monitoring systems.
While the notebook tool includes 308 cities, this paper focuses on the results for 96 cities with more than 200,000 inhabitants, with a particular emphasis on Paris, Munich, and Zurich. These cities are pilot cities for the Horizon 2020-funded project Pilot Application in Urban Landscapes ("ICOS Cities"), where a range of urban CO2 monitoring methods are being implemented and assessed. According to our analyses, Zurich — and Munich especially — should be less challenging to monitor than Paris. Examining the challenges individually reveals that the most significant relative challenge is the "modelling challenge" (c) for Zurich and Paris. Complex urban topography adds to the challenge for both cities, and in Zurich, the natural topography further amplifies the challenge. Munich has low scores across all challenges, but with the greatest challenge anticipated from the "application-specific observational challenge" (d). Overall, Bratislava (Slovakia) and Copenhagen (Denmark) are among the most distant from Paris, Munich, and Zurich in our dendrogram resulting from numerical cluster-analysis. This makes them strong candidates for inclusion in the ICOS Cities network, as they would potentially provide the most information on how to monitor emissions in cities that face different challenges.
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Status: open (until 03 Jul 2025)
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RC1: 'Comment on essd-2025-63', Anonymous Referee #1, 20 May 2025
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The scope of this paper is the monitoring of CO2 emissions of European cities from the atmospheric concentrations. Indeed, the emission from the cities increases the atmospheric concentrations so that concentration measurements can be used to infer the emissions. There are a number of challenges however linked to the emission-concentration relationship that depends on the variable atmospheric transport, the heterogeneity of the emission or the other (than the city emissions) fluxes that impact the CO2 concentrations. The challenges are different among cities and this paper attempts to classify the European cities according to these challenges. They have defined a number of indicators to quantify the various challenges and make a statistical analysis based on this criteria to identify the cities that are the most suitable for CO2 monitoring experiments.
The paper is interesting and can be of interest for the growing community that attempts to estimate city emissions either from surface network measurements of from remote sensing imagery. One could certainly criticize the definition of the challenge indicators but the choice of the authors appear reasonable.
Note that some challenge apply mostly to remote sensing applications (such as the cloud cover) whereas other are more applicable to the definition of use of a surface network (such as the wind direction). All indicators are made available so that one can make its own classification.
Minor comments to be considered by the authors:
Line 38 : Cities account for approximately : "account for" is not clear enough. Is it scope 1, scope 2 or scope 3 ? Only scope 1 emissions could be measured from the atmospheric concentrations
Line 52 : (such as kgCO2/vehicle), : It would make more sense to have kkCO2/km
Line 83 : in Indianapolis the enhancement is only about 3 ppm. Is it on average, or max ?
Line 148 : “about 50 out of 365 plumes per year could” . Better to say that, out of the 365 days in a year, only 50 appear suitable to observe the CO2 plume from space"
Line 149 : Furthermore, the collected samples were higher… Not clear. What is higher ? Emissions or CO2 plume ?
Line 153. might be monitored from ». "might be monitored" lacks detail. It depends on the accuracy requirement
Line 449 : make eddy covariance measurements ». I did not understood that this paper analyzes the possibility to make such measurements
Line 458 : What emissions from airport are considered ? Is it mostly that of the building or that of the plane take off ? For those, the temporal profile of the emissions may be quite challenging
Citation: https://doi.org/10.5194/essd-2025-63-RC1
Data sets
CO₂ Monitoring Challenges Notebook Package Ida Storm, Ute Karstens, and Claudio D'Onofrio https://doi.org/10.18160/P8SV-B99F
CO₂ Monitoring Challenges City Mapbooks Ida Storm, Ute Karstens, and Claudio D'Onofrio https://doi.org/10.18160/Z66D-05JT
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