the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 01 Apr 2025
Emission characteristics of greenhouse gases and air pollutants in a Qinghai-Tibetan Plateau city using a portable Fourier transform spectrometer and TROPOMI observations
Abstract. Despite the critical need to understand greenhouse gas and air pollutant concentrations and their emissions characteristics in urban and industrial areas, limited assessments have been conducted in the Qinghai-Tibetan Plateau (QTP) cities. Herein, for the first time, we present CO2, CH4 and CO column abundances using a portal Fourier-transform infrared spectrometer (EM27/SUN) in Ganhe Industrial Park (36.546° N, 101.518° E, 2603 m a.s.l.), located in the suburbs of Xining, Qinghai Province, during May – June 2024. Ground-based measurements found to be higher than spaceborne measurements (TROPOMI and IASI) and model forecast (CAMS) across all investigated species, indicating higher local emissions. Notably, significant discrepancies in CO levels are observed, particularly under easterly wind conditions, which transport polluted airmasses from Xining city. To further quantify emissions, we applied a simple dispersion model to the EM27/SUN data and TROPOMI products, estimating an average CO emission rate of 12.3 ± 9.6 kg/s and 8.9 ± 7.5 kg/s, respectively. A wind-assigned anomaly method further applied to the TROPOMI dataset yielded a CO emission rate of 8.5 kg/s. Additionally, the ground-based observations of ∆XCO/∆XCO2 ratio exhibits a strong correlation under easterly winds, which suggests an average CO2 emission rate of 550 kg/s from Xining city. These findings underscore the utility of portable FTIR spectrometers to enhance our understanding of urban emissions at QTP.
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Notice on discussion status
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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Preprint
(9688 KB)
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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- Final revised paper
Journal article(s) based on this preprint
Interactive discussion
Status: closed
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RC1: 'Comment on egusphere-2025-966', Anonymous Referee #1, 11 May 2025
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AC1: 'Reply on RC1', Qiansi Tu, 24 Oct 2025
We would like to thank reviewer #1 for taking the time to review this manuscript and for providing valuable, constructive feedback and corresponding suggestions that helped us to further improve the manuscript.
In this author's comment, all the points raised by the reviewer are copied here one by one and shown in black color, along with the corresponding reply from the authors in blue.
-
AC1: 'Reply on RC1', Qiansi Tu, 24 Oct 2025
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RC2: 'Comment on egusphere-2025-966', Anonymous Referee #2, 29 Jul 2025
This paper by Tu et al., focus on observations at an industrial park and simulations of Xining’s emissions using portable Fourier transform spectrometer and TROPOMI observations. The topic is interesting and falls into the scope of ACP. I have some major comments that may improve the quality of this paper.
Major concerns:
May-June may be too short to represent the whole year, and in winter there are coal-burning period for heating. Do the authors have longer time observations? Please at least add some discussions on this time coverage influences.
CAMS resolution and emissions information may be too sparse to include local emission areas and may not be appropriate for the comparison.
I suggest the authors include analyses and comparisons with open accessed inventory (e.g. MEIC). And add some discussions on the difference between inventory and inversions.
Spatial distributions associated with the TROPOMI data, simulations and inversions are needed to improve the content of this paper.
Besides the CO and CO2 emissions rates, the CH4 emissions rates are also important.
Minor comments:
- Add serial numbers to the subFiguresin Fig.1, and the font in subFigure2 is too small and difficult to read.
- line159: Does this sentencemeansthat CO and CO₂ come from different sources?
- Add more descriptions for Fig.2 (a,b,c). And for Figure3, do data from TROPOMI (5.5 km × 7 km) and COCCON (point) have comparable spatial representativeness? What processing methods were applied? These should be explicitly stated in the Methodsand in discussions.
- lines 193-195: Why not match the COCCON data with the grid scale of CAMS? At distances beyond 20km or even 50km, and the factors influencing observations or forecast results are local emission sources and atmospheric transport processes.
- Figure4b: The data points are overly clustered. It is recommended to reduce the range of the x-y axes, for instance to 1870-1950.And other subplots also need to be improved for this aspect.
- Figure4c: The legend should not overlaythe data plots.
- Line 205: To what extent is this underestimation a result of observation? Have youconsideredspatial representativeness inconsistency as a potential source？
- Line 217: Enhanced relative to what?
- lines 225-227: The definitions of background CO concentration and ∆XCO should be provided when these terms were firstlyappeared.
- Line 241-242: Hasthe higher emissionsled to the observed concentration peak?
12 Why only analyze the CO emission and the relation of ∆XCO and ∆XCO2? How about CH4?
13 Please have the manuscript polished again for grammar and spellings.
Citation: https://doi.org/10.5194/egusphere-2025-966-RC2 -
AC2: 'Reply on RC2', Qiansi Tu, 24 Oct 2025
We would like to thank reviewer #2 for taking the time to review this manuscript and for providing valuable, constructive feedback and corresponding suggestions that helped us to further improve the manuscript.
In this author's comment, all the points raised by the reviewer are copied here one by one and shown in black color, along with the corresponding reply from the authors in blue.
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2025-966', Anonymous Referee #1, 11 May 2025
This manuscript presents a short-term measurement campaign in Xining, a city located on the eastern edge of the Qinghai-Tibetan Plateau (QTP), using a portable FTIR instrument (EM27/SUN) to retrieve column-averaged concentrations of CO₂, CH₄, and CO. The study touches on several topics, including satellite validation for CH₄ and CO, CAMS product evaluation, combustion efficiency derived from the CO:CO₂ ratio, and CO₂ emissions estimation. However, the manuscript lacks a cohesive narrative and frequently shifts between topics without adequately developing or concluding each one. As a result, it reads more like a collection of loosely connected sub-studies rather than a focused, hypothesis-driven investigation.
And the study does not offer significant methodological innovation or new scientific insights and lacks discussion part. The only potentially unique aspect is the absence of previous atmospheric column observations in the suburban area of Xining city. However, this alone does not justify publication unless the authors can thoroughly address the concerns outlined above through major revisions.Major Comments:
1. The observational period was very short, only 8 days in early June 2024, but the rationale for selecting this specific timeframe is unclear. Was there a particular emission event or atmospheric condition of interest during this period? The motivation for the campaign is not well explained. Additionally, the measurements were conducted in a suburban area, but it is not clear whether this site is representative, whether the data can inform future carbon cycle studies, or what the broader scientific significance is. Summer conditions are typically associated with various interfering factors, yet these are neither acknowledged nor discussed in the manuscript.
2. The manuscript aims to evaluate satellite retrievals using ground-based observations. In Section 2.2, a detailed description of the general COCCON product is provided, but there is almost no information about the specific EM27/SUN instrument used in this campaign. Key details such as the instrument’s stability before and after the measurement period, the configuration of retrieval parameters, and whether any calibration was performed using TCCON, AirCore, or aircraft measurements are missing. Additionally, the measurement uncertainty is not discussed. As the ground-based observations serve as the reference for satellite validation, it is essential to present their accuracy and reliability clearly, rather than focusing only on general background information.
3. The manuscript also attempts to evaluate CAMS simulation results using ground-based observations. However, the approach raises several questions. A 20 km radius was used for CAMS product validation—but why? Since CAMS provides data at specific grid points, the rationale for selecting a 20 km averaging radius is unclear. Large-area averaging is typically applied in satellite validation to reduce observational noise and improve sampling statistics, but it is not obvious why a 20 km radius was appropriate or necessary in this case.
Moreover, the analysis in this section is not sufficiently developed. The authors conclude that CAMS performs well in simulating CH₄ in this region, while its performance for CO and CO₂ is poor. But what are the broader implications of this result? Does this indicate that CAMS is better suited for CH₄ studies over the Qinghai-Tibetan Plateau? Could this support the case for establishing long-term observation sites in the area?
In addition, satellite retrievals usually offer a higher number of soundings than ground-based instruments. How does the CAMS product compare with satellite observations in terms of coverage and consistency? Do the conclusions drawn from CAMS agree with those from the COCCON dataset? These questions are not clearly addressed in the manuscript.4. The manuscript estimates CO₂ emissions using CO fluxes derived from TROPOMI and EM27/SUN observations. However, several points need clarification. First, the emission estimation appears to be based on multiplying values observed by wind speed. If so, does this method account for transport processes and particle dilution along the plume? A brief explanation of this approach in the Methods section would be helpful. In addition, how does the CO:CO₂ ratio used in this study compare with values reported in emission inventories? This could be further discussed, especially in relation to combustion efficiency and source attribution.
Moreover, there appears to be an inconsistency in logic. In the CAMS evaluation, CO and CO₂ simulations were shown to perform poorly, while CH₄ agreed well with observations. However, in this section, the calculated CO emissions match CAMS values, and CO₂ emissions align closely with inventory estimates. Given the earlier performance issues with CO and CO₂ in CAMS, this raises questions about the reliability of the derived fluxes. What about CH₄ emissions in this context? Without addressing this discrepancy, the conclusions are difficult to reconcile.Minor Comments:
Line 53–55: I still do not fully understand the claim that surface observations are influenced by surface exchange but limit the ability to estimate sources and sinks. Given that surface measurements are sensitive to near-surface fluxes, wouldn’t they actually be more effective for detecting local sources and sinks? This statement needs clarification.
Line 161: This point raises concerns. The measurement period spans only 8 days, while coal mining activity is often highly episodic. As shown in the study by [Author] (https://doi.org/10.1016/j.ecolind.2023.110454), regions similar to the study area in Qinghai are known to have significant coal mining emissions. Therefore, concluding that there is no such influence based solely on this short observational window appears premature.Citation: https://doi.org/10.5194/egusphere-2025-966-RC1 -
AC1: 'Reply on RC1', Qiansi Tu, 24 Oct 2025
We would like to thank reviewer #1 for taking the time to review this manuscript and for providing valuable, constructive feedback and corresponding suggestions that helped us to further improve the manuscript.
In this author's comment, all the points raised by the reviewer are copied here one by one and shown in black color, along with the corresponding reply from the authors in blue.
-
AC1: 'Reply on RC1', Qiansi Tu, 24 Oct 2025
-
RC2: 'Comment on egusphere-2025-966', Anonymous Referee #2, 29 Jul 2025
This paper by Tu et al., focus on observations at an industrial park and simulations of Xining’s emissions using portable Fourier transform spectrometer and TROPOMI observations. The topic is interesting and falls into the scope of ACP. I have some major comments that may improve the quality of this paper.
Major concerns:
May-June may be too short to represent the whole year, and in winter there are coal-burning period for heating. Do the authors have longer time observations? Please at least add some discussions on this time coverage influences.
CAMS resolution and emissions information may be too sparse to include local emission areas and may not be appropriate for the comparison.
I suggest the authors include analyses and comparisons with open accessed inventory (e.g. MEIC). And add some discussions on the difference between inventory and inversions.
Spatial distributions associated with the TROPOMI data, simulations and inversions are needed to improve the content of this paper.
Besides the CO and CO2 emissions rates, the CH4 emissions rates are also important.
Minor comments:
- Add serial numbers to the subFiguresin Fig.1, and the font in subFigure2 is too small and difficult to read.
- line159: Does this sentencemeansthat CO and CO₂ come from different sources?
- Add more descriptions for Fig.2 (a,b,c). And for Figure3, do data from TROPOMI (5.5 km × 7 km) and COCCON (point) have comparable spatial representativeness? What processing methods were applied? These should be explicitly stated in the Methodsand in discussions.
- lines 193-195: Why not match the COCCON data with the grid scale of CAMS? At distances beyond 20km or even 50km, and the factors influencing observations or forecast results are local emission sources and atmospheric transport processes.
- Figure4b: The data points are overly clustered. It is recommended to reduce the range of the x-y axes, for instance to 1870-1950.And other subplots also need to be improved for this aspect.
- Figure4c: The legend should not overlaythe data plots.
- Line 205: To what extent is this underestimation a result of observation? Have youconsideredspatial representativeness inconsistency as a potential source？
- Line 217: Enhanced relative to what?
- lines 225-227: The definitions of background CO concentration and ∆XCO should be provided when these terms were firstlyappeared.
- Line 241-242: Hasthe higher emissionsled to the observed concentration peak?
12 Why only analyze the CO emission and the relation of ∆XCO and ∆XCO2? How about CH4?
13 Please have the manuscript polished again for grammar and spellings.
Citation: https://doi.org/10.5194/egusphere-2025-966-RC2 -
AC2: 'Reply on RC2', Qiansi Tu, 24 Oct 2025
We would like to thank reviewer #2 for taking the time to review this manuscript and for providing valuable, constructive feedback and corresponding suggestions that helped us to further improve the manuscript.
In this author's comment, all the points raised by the reviewer are copied here one by one and shown in black color, along with the corresponding reply from the authors in blue.
Peer review completion
Journal article(s) based on this preprint
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Qiansi Tu
Frank Hase
Ying Zhang
Jiaxin Fang
Yanwu Jiang
Xiaofan Li
Matthias Schneider
Zhuolin Yang
Xin Zhang
Zhengqiang Li
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(9688 KB) - Metadata XML
This manuscript presents a short-term measurement campaign in Xining, a city located on the eastern edge of the Qinghai-Tibetan Plateau (QTP), using a portable FTIR instrument (EM27/SUN) to retrieve column-averaged concentrations of CO₂, CH₄, and CO. The study touches on several topics, including satellite validation for CH₄ and CO, CAMS product evaluation, combustion efficiency derived from the CO:CO₂ ratio, and CO₂ emissions estimation. However, the manuscript lacks a cohesive narrative and frequently shifts between topics without adequately developing or concluding each one. As a result, it reads more like a collection of loosely connected sub-studies rather than a focused, hypothesis-driven investigation.
And the study does not offer significant methodological innovation or new scientific insights and lacks discussion part. The only potentially unique aspect is the absence of previous atmospheric column observations in the suburban area of Xining city. However, this alone does not justify publication unless the authors can thoroughly address the concerns outlined above through major revisions.
Major Comments:
1. The observational period was very short, only 8 days in early June 2024, but the rationale for selecting this specific timeframe is unclear. Was there a particular emission event or atmospheric condition of interest during this period? The motivation for the campaign is not well explained. Additionally, the measurements were conducted in a suburban area, but it is not clear whether this site is representative, whether the data can inform future carbon cycle studies, or what the broader scientific significance is. Summer conditions are typically associated with various interfering factors, yet these are neither acknowledged nor discussed in the manuscript.
2. The manuscript aims to evaluate satellite retrievals using ground-based observations. In Section 2.2, a detailed description of the general COCCON product is provided, but there is almost no information about the specific EM27/SUN instrument used in this campaign. Key details such as the instrument’s stability before and after the measurement period, the configuration of retrieval parameters, and whether any calibration was performed using TCCON, AirCore, or aircraft measurements are missing. Additionally, the measurement uncertainty is not discussed. As the ground-based observations serve as the reference for satellite validation, it is essential to present their accuracy and reliability clearly, rather than focusing only on general background information.
3. The manuscript also attempts to evaluate CAMS simulation results using ground-based observations. However, the approach raises several questions. A 20 km radius was used for CAMS product validation—but why? Since CAMS provides data at specific grid points, the rationale for selecting a 20 km averaging radius is unclear. Large-area averaging is typically applied in satellite validation to reduce observational noise and improve sampling statistics, but it is not obvious why a 20 km radius was appropriate or necessary in this case.
Moreover, the analysis in this section is not sufficiently developed. The authors conclude that CAMS performs well in simulating CH₄ in this region, while its performance for CO and CO₂ is poor. But what are the broader implications of this result? Does this indicate that CAMS is better suited for CH₄ studies over the Qinghai-Tibetan Plateau? Could this support the case for establishing long-term observation sites in the area?
In addition, satellite retrievals usually offer a higher number of soundings than ground-based instruments. How does the CAMS product compare with satellite observations in terms of coverage and consistency? Do the conclusions drawn from CAMS agree with those from the COCCON dataset? These questions are not clearly addressed in the manuscript.
4. The manuscript estimates CO₂ emissions using CO fluxes derived from TROPOMI and EM27/SUN observations. However, several points need clarification. First, the emission estimation appears to be based on multiplying values observed by wind speed. If so, does this method account for transport processes and particle dilution along the plume? A brief explanation of this approach in the Methods section would be helpful. In addition, how does the CO:CO₂ ratio used in this study compare with values reported in emission inventories? This could be further discussed, especially in relation to combustion efficiency and source attribution.
Moreover, there appears to be an inconsistency in logic. In the CAMS evaluation, CO and CO₂ simulations were shown to perform poorly, while CH₄ agreed well with observations. However, in this section, the calculated CO emissions match CAMS values, and CO₂ emissions align closely with inventory estimates. Given the earlier performance issues with CO and CO₂ in CAMS, this raises questions about the reliability of the derived fluxes. What about CH₄ emissions in this context? Without addressing this discrepancy, the conclusions are difficult to reconcile.
Minor Comments:
Line 53–55: I still do not fully understand the claim that surface observations are influenced by surface exchange but limit the ability to estimate sources and sinks. Given that surface measurements are sensitive to near-surface fluxes, wouldn’t they actually be more effective for detecting local sources and sinks? This statement needs clarification.
Line 161: This point raises concerns. The measurement period spans only 8 days, while coal mining activity is often highly episodic. As shown in the study by [Author] (https://doi.org/10.1016/j.ecolind.2023.110454), regions similar to the study area in Qinghai are known to have significant coal mining emissions. Therefore, concluding that there is no such influence based solely on this short observational window appears premature.