the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 05 Oct 2023
Optimising Urban Measurement Networks for CO2 Flux Estimation: A High-Resolution Observing System Simulation Experiment using GRAMM/GRAL
Sanam N. Vardag
Robert Maiwald
Abstract. To design a monitoring network for estimating CO2 fluxes in an urban area, a high-resolution Observing System Simulation Experiment (OSSE) is performed using the transport model Graz Mesoscale Model (GRAMMv19.1) coupled to the Graz Lagrangian Model (GRALv19.1). First, a high-resolution anthropogenic emission inventory, which is considered as the truth serves as input to the model to simulate CO2 concentration in the urban atmosphere on 10 m horizontal resolution in a 12.3 km x 12.3 km domain centered in Heidelberg, Germany. By sampling the CO2 concentration at selected stations and feeding the measurements into a Bayesian inverse framework, CO2 fluxes on neighbourhood scale are estimated. Different configurations of possible measurement networks are tested to assess the precision of posterior CO2 fluxes. We determine the trade-off of between quality and quantity of sensors by comparing the information content for different set-ups. Decisions on investing in a larger number or more precise sensors can be based on this result. We further analyse optimal sensor locations for flux estimation using a Monte Carlo approach. We examine the benefit of additionally measuring carbon monoxide. We find that including CO as tracer in the inversion allows the disaggregation of different emission sectors such as traffic emissions. Finally, we quantify the benefit of introducing a temporal correlation into the prior emissions. The results of this study give implications for an optimal measurement network design for a city like Heidelberg. The study showcases the general usefulness of the developed inverse framework using GRAMM/GRAL for planning and evaluating measurement networks in an urban area.
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Sanam N. Vardag and Robert Maiwald
Status: final response (author comments only)
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RC1: 'Comment on gmd-2023-192', Anonymous Referee #1, 22 Oct 2023
General comments:
This study presents a study on the placement of a monitoring network based on a process model to optimize CO2 carbon fluxes in urban areas. The research addresses several critical aspects related to the precision and effectiveness of the proposed measurement network, including sensor quantity and quality, optimal sensor locations, the potential inclusion of carbon monoxide (CO) measurements, and the introduction of temporal correlations into prior emissions. A notable strength of the study is its practical relevance, aiming to inform decisions concerning sensor deployment in real-world urban settings, with Heidelberg, Germany, serving as a case study. This approach has the potential to guide similar efforts in other urban areas, making it of interest to both researchers and policymakers. However, there are a few aspects that could benefit from further attention or clarification in the manuscript. For instance, providing insights into potential challenges or limitations of the proposed approach, such as data availability and cost considerations, would be valuable for readers seeking to replicate or adapt the methodology. In summary, this study constitutes a valuable contribution to the field of urban CO2 flux estimation and measurement network design. With minor improvements in the clarity of methodology and the consideration of potential limitations, it has the potential to be a valuable reference for both researchers and practitioners involved in urban environmental monitoring and management.
Specific comments:
Page 2 and Line 36: Clarity on Observing System Simulation Experiments (OSSEs), When introducing Observing System Simulation Experiments (OSSEs), provide additional context for readers who may not be familiar with this term. Explain briefly how OSSEs work and their role in assessing monitoring networks.
Page 2: Consider providing definitions or explanations for key terminology used in the introduction, such as “pseudo observations” and “Jacobian” This will aid readers in understanding the technical aspects of your research.
Line 100: Mentioning that the wind field resolution for GRAL is 2m with a total of 200 cells is informative, but you could briefly explain why this level of detail was selected and how it impacts the model's accuracy or performance.
Consider optimizing Figure 1 by suggesting that the GRAL domain be displayed directly within the GRAMM domain. Also, shows the basic outline of the city of Heidelberg. Additionally, supplement the horizontal and vertical coordinate headings with the units of latitude and longitude, and include a legend.
Line 202: Explain the significance of using administrative districts as emission groups in your study. How does this choice impact the optimization process, and why were small districts and border districts aggregated?
Line 216: TNO Abbreviation: Clarify what TNO represents (if it's an abbreviation).
Line 226: It may be helpful to add a brief explanation of Monte Carlo experiments and the analysis process to elucidate the concept for readers unfamiliar with Monte Carlo experiments.
Refine the information on Figure 2 map sheets and ensure that maps include a legend, latitude, longitude, and compass information.
The discussion of the uncertainty analysis of the overall model development is somewhat sparse and scattered. There is a need for additional integration of the discussion of uncertainty.
Subfigures Numbering: Add numbers and subfigure titles to subfigures in some groupings. For example, Figures 2, 3, 4, and 6.
Modify “a.)” in the figure captions to “(a)” to indicate this, and follow the same format for other subfigures.
I read your paper published in 2015 "Vardag, S. N., Gerbig, C., Janssens-Maenhout, G., and Levin, I.: Estimation of continuous anthropogenic CO2: a model-based evaluation of CO2/CO, CO, δ13C-CO2, and Δ14C-CO2 tracer methods, Atmospheric Chemistry and Physics, 15, 12705-12729, https://doi.org/10.5194/acp-15-12705-2015, 2015." and comments from reviewer Jocelyn Turnbull. I found the comment that the study by Vardag et al.,(2015) suggests that in Europe, CO may not be as available as a tracer of fuel CO2 as it is in other regions due to the low ratio of CO:CO2 emissions from European transportation. In contrast, in section 3.3 of this study, the tracer role of CO is emphasized, especially in distinguishing between different emission sources, such as transportation emissions. However, the fact that transportation emissions are generated from fuels, which includes emitted CO2. there is a need for further clarification or discussion as to what causes this discrepancy.
The conclusions section is quite long, and some of its content overlaps with the results and discussion. Consider optimizing the structure of the manuscript, and it is recommended to add a separate discussion section. The conclusions should be summarized insights based on the results of the entire study.
Citation: https://doi.org/10.5194/gmd-2023-192-RC1 - AC3: 'Reply on RC1', Sanam Noreen Vardag, 01 Dec 2023
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CEC1: 'Executive editor comment on gmd-2023-192', Astrid Kerkweg, 23 Oct 2023
Dear authors,
we will have to change to manuscript type of your article, as the “Model experiment description paper” is mostly meant for the description of large model intercomparison projects. You are definitely publishing a model development. Therefore, I will ask copernicus office to assign it to the “Development and technical paper” type.
Best regards,
Astrid Kerkweg
Citation: https://doi.org/10.5194/gmd-2023-192-CEC1 -
AC1: 'Reply on CEC1', Sanam Noreen Vardag, 24 Oct 2023
Dear Astrid Kerkweg,
thanks for pointing this out and asking for assignment to “Development and technical paper” type. We gladly follow your suggestion.
Kind regards,
Sanam VardagCitation: https://doi.org/10.5194/gmd-2023-192-AC1
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AC1: 'Reply on CEC1', Sanam Noreen Vardag, 24 Oct 2023
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RC2: 'Comment on gmd-2023-192', Gerrit H. de Rooij, 17 Nov 2023
Review of GMD-2023-192. Optimising Urban Measurement Networks for CO2 Flux Estimation: A High-Resolution Observing System Simulation Experiment using GRAMM/GRAL, by Vardag and Maiwald.
The paper presents the use of a model for atmospheric transport of CO2 in an urban area to estimate CO2 emissions from a set of locally aggregated sources within the model domain of a case study. It explains how the quality of the estimates depends on the configuration of a CO2 sensor network, and on the precision of the individual sensors in that network. This information can be used to optimize sensor networks in urban areas.
The paper is well structured, and the science reported is worth publishing. I had some difficulty with the English from time to time, though. Also, the explanations and the line of thought of some sections were difficult to follow. I added many small comments to the manuscript where I suggest alternative formulations or ask for clarification. The more substantial comments are repeated below for clarity.
Overall, I think the paper does not need any reworking of the work on which the reporting is based, but the text and, to a limited extent, the figures, will need some rewriting and editing. When doing so, please make the captions of the table and the figures more explanatory so they can be read and understood stand-alone. I therefore recommend minor revisions.
General comments
Many acronyms appear in the paper. Please collect them in a list for easy reference. This will also resolve the issue that not all of them are explained on first use.
You sometimes switch between simple past tense and simple present tense within a paragraph for no obvious reason. Please go over the paper to ensure consistency.
Please explain how the term ‘state’ is defined. The term appears frequently, but it is not always clear what exactly is meant by it. I believe it means the CO2 emissions (in what units?) by each emission group, but I am not sure.
You do not discuss the effect of CO2 transfers across boundary of the modelled domain. Do these fluxes need to be taken into consideration?
Please explain what TNO data are.
The sensor network optimization does not include the possibility of installing more precise sensors in areas with large CO2 emissions and cheaper sensors in areas with low emissions, but it seems to me an approach worth exploring. It would allow you, for instance, to minimize the absolute measurement error of the entire network. Or do the agencies/departments operating such networks gravitate toward networks with sensors of a single type?
Specific comments
Table 1 could use some more explanation it its caption, for instance about the the last column and its units, and why there are two categories for Road Transport diesel.
Figure 2:
Is the color scale well chosen?. In the top row everything and in the prior column everything is zero.
I do not understand why there are white spaces in row 2. Should not the entire area be covered with pixels?
L. 263-265:
I think it would be good to discuss the potential effect on the optimal sensor network this simplification (ignoring background concentrations and biogenic CO2 sources) might have. When a network is to be implemented in a real-life situation, there is no way to exclude certain sources - the measured CO2 concentrations will be influenced by all existing sources and sinks.
Figure 3 and later figures:
The horizontal axis only states ‘State’, but it is a bar graphs. Do the bars represent emission groups?
Also: please include more tick marks, and have them on all sides.
Figure 5:
You need to explain a bit how to read and use this figure. Also explain that this figure only applies to a particular configuration in a particular location.
I suppose this figure only becomes useful once sensor prices and installation/maintenance costs are available. You can then create a similar map with the total cost of a set of y sensors with noise x. A given budget will identify wich squares on the map can be afforded. You can then go Fig. 5 and pick from this subset the square with the highest relative improvement.
L. 403-404:
Including spatial correlation lengths in the future does not logically follow from the effectiveness of having temporal correlations in your model. Wind directions and velocities vary strongly, which will affect spatial correlation lengths. The ticking of the clock and the daily cycle of basically everything vary considerably less.
Formulated more informally: Temporal correlations of CO2 emissions in Heidelberg are driven for a large part by the heavily synchronized time schedules of humans in a developed society. The wind is not bound by such constraints.
- AC2: 'Reply on RC2', Sanam Noreen Vardag, 01 Dec 2023
Sanam N. Vardag and Robert Maiwald
Model code and software
Bayesian inversion R. Maiwald https://doi.org/10.5281/zenodo.8354902
Experiments R. Maiwald https://github.com/ATMO-IUP-UHEI/Experiments
Processing GRAMM/GRAL output R. Maiwald https://zenodo.org/record/8375169
Sanam N. Vardag and Robert Maiwald
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