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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RC1: 'Comment on essd-2025-63', Anonymous Referee #1, 20 May 2025
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 -
RC2: 'Comment on essd-2025-63', Anonymous Referee #2, 23 Aug 2025
# Review of essd-2025-63: "Monitoring CO₂ in diverse European cities: Highlighting needs and challenges through characterisation"
## 1. General Comments
This paper presents a timely framework, the "CO₂ Monitoring Challenges
City Mapbooks" (CMC-CITYMAP), for characterizing and classifying
European cities based on the challenges they pose to urban CO₂
monitoring. The manuscript is well-structured, and the methodology is
generally well-explained. The authors use a systematic approach to
quantify four key challenges (background, biogenic, modelling, and
observational) with 18 metrics derived from public datasets, providing a
powerful tool for network design and strategic planning.However, the manuscript's primary weakness is the lack of a clear,
reproducible justification for the weighting scheme used to combine the
18 metrics into the four challenge scores. The weights are central to
the analysis, yet their derivation is opaquely described as being
"assigned based on expert knowledge and consideration of our literature
review" without clear descriptions (line 386). This subjectivity
undermines the framework's quantitative and objective claims.Addressing this major point is essential for publication. Additionally,
a more in-depth discussion of the study's limitations, particularly
regarding city boundary definitions and the selection of metrics, would
significantly enhance the scientific rigor, reproducibility, and impact
of this important work.## 2. Detailed Comments
### **Weighting of Metrics (Major Point)**
The manuscript's most significant issue is the insufficient
justification for the weights assigned to each of the 18 metrics (Tables
1 and 2). The claim that they are based on "expert knowledge" is not
sufficient for a scientific paper aiming to establish a quantitative
framework. To address this, the authors should:- **Provide a detailed rationale:** Include an appendix or
supplementary material that explains the reasoning for each specific
weight. For example, why is "Share of dominant wind" (30%)
considered three times more important than "Share of wind \>2 m/s"
(10%) for the background challenge?
- **Perform a sensitivity analysis:** Ideally, demonstrate or at least
give examples of how the final challenge scores and city rankings
change under different plausible weighting schemes. This would show
the robustness of the conclusions.
- **Discuss alternative methods:** Discuss other, more objective
methods for determining weights, even if not implemented (e.g.,
Principal Component Analysis). If these are too complex, simpler,
clearer methods should be considered, or at least suggested. The
authors should justify why the current approach was chosen over
these alternatives.### **Methodology & Justification**
- **City Definition and Boundaries:** The authors correctly note that
the OECD functional urban area definition can lead to significant
issues, such as excluding major emission sources like the Zurich
airport (lines 555-556). The discussion (Section 4) should be
expanded to explore the quantitative impact of this. For instance,
how much would the "modelling" or "background" challenge scores for
Zurich change if the airport were included or excluded? It would
also be beneficial to show a brief example of how the proposed
"carbon cities" alternative would alter the metrics for one city.
- **Normalization:** While the 10-90 percentile range for min-max
normalization is a reasonable choice to reduce the influence of
outliers, the impact of this choice should be briefly stated. For
example, how many cities fall outside this range for key metrics,
and how does this affect their final scores?### **Specific Challenges & Metrics**
- **"Application-Specific Observational Challenge":** This category
feels heterogeneous, combining challenges for two different
measurement systems: satellite (cloud cover) and radiocarbon
(nuclear masking/bias). As the authors suggest (lines 614-616),
these should be treated as separate challenges. I recommend
splitting this into a "Satellite Observation Challenge" and a
"Radiocarbon (¹⁴C) Challenge" to provide more targeted and
actionable information.
- **Biogenic Challenge:** The metrics used are NEE-related and
vegetation heterogeneity. However, the influence of urban-specific
biogenic factors (e.g., irrigation, urban heat island effects on
phenology) is not explicitly captured. The authors should be more
explicit about these limitations in the main text when introducing
the challenge.
- **Clarification Needed on Wind Speed:** The following statement
needs clarification: "Higher wind speeds result in larger influence
regions ("footprints"), which reduce the impact of strong local
sources that could interfere with the goal of obtaining spatially
representative observations. This leads to generally better
agreement between modeled and observed values" (Lines 312--313). Is
this influence related to the background region? If so, the sentence
needs to be clearer. Subtracting a large, uncertain background from
an observation can reduce the local signal, making it more
uncertain. If the point is about the dilution of the local signal
due to high winds, this is a separate issue from the background.
These sentences need to be presented more clearly.
- **Missing Metrics:** Was the inclusion of other potentially relevant
metrics considered? For example, for the "modelling challenge," were
metrics for urban canyons or street-level aspect ratios considered?
For the "background challenge," was population density in the upwind
buffer zone considered as a proxy for diffuse emissions? A brief
mention of why certain metrics were considered but ultimately
excluded would be helpful.### **Results & Discussion**
- **Results of Munich and Zurich (lines 452--453):** While a strong
dominant wind direction can help estimate boundary conditions, it
may also prevent the sampling of emission sources across a city
unless there are enough sampling sites. The authors do not seem to
consider this. It is also very challenging to model emissions for a
region with a strong dominant wind direction, as modeled wind biases
can lead to significant misestimations, e.g., due to the omission of
sources.
- **Clarification Needed on Low Wind Speed:** The following sentence
needs to be explained more clearly: "However, compared to the other
cities, the wind speed is quite frequently below 2 m s⁻¹, which
could be a challenge for collecting spatially representative up-wind
observations." (Lines 453--455).
- **Discussion of Paris (lines 584-599):** The comparison of the
paper's findings for Paris with results from Lian et al. (2023) is
excellent and provides strong validation for the framework. This
section could serve as a model for expanding the discussion of other
cities or challenges.
- **Conclusion:** The conclusion rightly points out that the results
are useful for identifying similarities between cities. However,
there is little discussion about how to mitigate the identified
challenges. Adding a discussion of mitigation strategies would
strengthen the paper.### **Presentation & Clarity**
- **Figure 3 (Challenge Scores):** This is an effective figure. A
minor suggestion for panel (a) is to add the numerical score for
each of the three pilot cities next to their bars to make comparison
with Table 3 easier.
- **Table 1:** This table could be improved with some explanatory
comments. For example, an asterisk could note that the weights
within each category sum to 1. The meaning of some metrics is not
immediately clear from the table, such as "Share of wind from
dominant wind direction (limited to \>2 m s−1)". It is also not
clear if a higher or lower share is better and why (after reading
the paper more, it is better, but providing more information at the
beginning would help the reader).
- **Metric Explanations:** Does Section 2.2 explain all the metrics in
Table 1? The section seems to mix data source and metric
descriptions, and some metrics appear to be missing a clear
explanation.
- **Equations:**
- **Equation 1:** The term `x_{i,j}` is not explained. The index
`j` represents "characteristics," but the paper states, "These
metrics represent specific characteristics of the city" (Line
158), implying that characteristics are the metrics. This should
be clarified, although it appears to be.
- **Equation 3:** This equation needs more explanation. The term
`x_i` (or `y_i`) appears to be a vector. How is it formed? For
example, for the background challenge, does the vector `x` have
5 elements? This is not clear from the text, although it seems
so.
- **Typos and Grammar:** There are several typos (e.g., "with an
emphasize on the surrounding" in Line 228) and long sentences that
could be revised to improve readability.In summary, this paper makes a valuable contribution to the field of
urban GHG monitoring. Addressing the major point regarding the
justification of the metric weights, along with the other detailed
comments, will elevate the manuscript to a more impactful publication.Citation: https://doi.org/10.5194/essd-2025-63-RC2
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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