Comments on Chen et al – 2025-377

Isotopic signatures of methane emissions from oil and natural gas plants in southwestern China.

General comments

This paper provides important new isotopic measurements from methane emitted by China’s Oil and Gas sector. China is the world’s largest emitter of anthropogenic methane and isotopic data are essential if Chinese emissions are to be quantified by sector. Thus these new measurements are very valuable indeed.

The paper should certainly be published. That said, there are a number of problems with the manuscript as it stands at the moment and it needs to be revised before final acceptance.

Specific points

The introduction needs to be heavily rewritten between lines 37-107. It reads rather like something written at the start of the project some years ago and lightly updated. This is a very active field and many important recent references are missing, while a lot of good but very elderly papers are still cited. I would strongly suggest shortening this section (L37-107) by perhaps half and making it much more modern.

I have added a list of papers that might be considered below. In particular I would draw attention to the ongoing work by Saunois et al, most recently in 2024/5. Maybe the International Energy Agency should be cited for China’s total methane emissions. The state of methane should be updated to 2024 – see Michel et al. 2024 and Nisbet et al 2025. Given the focus on field measurement of isotopes, maybe there is one older reference (Dlugokencky et al. 2011) but many new papers.

From line 106-129 the introduction gets specific. That’s good, but maybe there should be a paragraph on the power of isotopes.

Line 148 – Paddy fields – more information needed here. This is important because the isotopes help discriminate between ricefield methane and fossil methane. Also how many cows and how much pig manure is in the region, and how many landfills. Another major factor is biomass burning, that can give very heavy methane (as in some later results in the paper).

Line 167 – maybe have a paragraph break here.

Line 196 to 208 in Section 2.2 – no information is given about time of day and diurnal variation in the height of the boundary layer, yet this is obviously important to the later discussion.

Line 243-261 Hysplit - How much local diurnal undeHrstanding is there for the movement of the boundary layer?  Is there any information about the stability of the air masses during UAV sampling? Pasquill stability classes?

Line 228 – Keeling plot. What line regression is being used? Maybe see Akritas and Bershady (1996) as used in France et al 2016 (see below for details)

Section 3.1 is the core of the paper and very valuable.

Section 3.2 has no mention of time of day or diurnal evolution of the boundary layer. Also there is no real discussion of other local sources including rice and animals (isotopically light) and crop waste and other biomass fires (heavy). Some of the heavy values (e.g. in L356 could be from local fires. However the very heavy value directly measured in L361 is indeed interesting. Overall I think this section 3.2 of the paper needs a fairly major reevaluation.

Line 331 percentages are quoted to a precision far beyond the real uncertainty. About half and about a fifth to a quarter might be a more accurate statement.

Line 365 onwards. The discussion should take into account other local sources – rice, animals, fires, and perhaps coal use. Fig 5 would be useful also a Table. Line 401 linear regression method not specified – see France et al / Ahritas and Bershady method.

Line 451 onwards – global comparision – see references below.

CONCLUSION

This paper present important new results that will be very useful in attributing China’s methane emissions to specific sources. The work should certainly be published. But the paper needs some work still.

REFERENCES to consider: don’t cite all but pick and choose which fit best in the text as it is revised.

SPECIFIC Oil and gas and Keeling

Al-Shalan, Aliah, et al. "Methane emissions in Kuwait: Plume identification, isotopic characterisation and inventory verification." Atmospheric Environment 268 (2022): 118763.

Akritas, M. G., and M. A. Bershady (1996), Linear regression for astronomical data with measurement errors and intrinsic scatter, Astrophys. J., 470(2), 706–714, doi:10.1086/177901.

Andersen, Truls, et al. "Local to regional methane emissions from the Upper Silesia Coal Basin (USCB) quantified using UAV-based atmospheric measurements." Atmospheric Chemistry and Physics https://doi.org/10.5194/acp-23-5191-2023

Ars, Sébastien, et al. "Using in situ measurements of δ13C in methane to investigate methane emissions from the western Canada sedimentary basin." Atmospheric Environment: X 23 (2024): 100286.

Chen, Zichong, et al. "Methane emissions from China: a high-resolution inversion of TROPOMI satellite observations." Atmospheric Chemistry and Physics 22.16 (2022): 10809-10826.

Dlugokencky, Edward J., et al. "Global atmospheric methane: budget, changes and dangers." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 369.1943 (2011): 2058-2072.

Fisher, Rebecca E., et al. "Measurement of the 13C isotopic signature of methane emissions from northern European wetlands." Global Biogeochemical Cycles 31.3 (2017): 605-623.

Fisher, Rebecca E., et al. "Arctic methane sources: Isotopic evidence for atmospheric inputs." Geophysical Research Letters 38.21 (2011).

France, James L., et al. "Measurements of δ13C in CH4 and using particle dispersion modeling to characterize sources of Arctic methane within an air mass." Journal of Geophysical Research: Atmospheres 121.23 (2016): 14-257.

International Energy Agency (2024) Global Methane Tracker: Methane emissions from energy. https://www.iea.org/reports/global-methane-tracker-2024/key-findings

Jacob, Daniel J., et al. "Quantifying methane emissions from the global scale down to point sources using satellite observations of atmospheric methane." Atmospheric Chemistry and Physics 22.14 (2022): 9617-9646.

Riddick, Stuart N., et al. "A quantitative comparison of methods used to measure smaller methane emissions typically observed from superannuated oil and gas infrastructure." Atmospheric Measurement Techniques 15.21 (2022): 6285-6296.

Riddick, Stuart N., et al. "Methane emissions from abandoned oil and gas wells in Colorado." Science of The Total Environment 922 (2024): 170990.

Zazzeri, G., et al. "Plume mapping and isotopic characterisation of anthropogenic methane sources." Atmospheric Environment 110 (2015): 151-162.

Zazzeri, Giulia, et al. "Carbon isotopic signature of coal-derived methane emissions to the atmosphere: from coalification to alteration." Atmospheric Chemistry and Physics 16.21 (2016): 13669-13680.

GLOBAL budget

Michel, Sylvia Englund, et al. "Rapid shift in methane carbon isotopes suggests microbial emissions drove record high atmospheric methane growth in 2020–2022." Proceedings of the National Academy of Sciences 121.44 (2024): e2411212121.

Nisbet, Euan G., et al. "Practical paths towards quantifying and mitigating agricultural methane emissions." Proceedings A. Vol. 481. No. 2309. The Royal Society, 2025.

Nisbet, Euan G., et al. "Atmospheric methane: Comparison between methane's record in 2006–2022 and during glacial terminations." Global Biogeochemical Cycles 37.8 (2023): e2023GB007875.

Nisbet, Euan G. "New hope for methane reduction." Science 382.6675 (2023): 1093-1093.

Saunois, Marielle, et al. "Global methane budget 2000–2020." Earth System Science Data Discussions 2024 (2024): 1-147.

The paper explores the isotopic signature of the oil and gas (ONG) sector in China, one of the major sources of methane emissions. The scientific methods used in the study are sound, and the results reveal some interesting findings. However, several aspects require improvement:

• The manuscript requires a thorough review for grammatical errors, especially in the introduction, which needs significant rewriting. Although some errors are noted in the specific comments below, this is not an exhaustive list, and the entire manuscript would benefit from careful revision.
• The source signatures of other methane sources (for example: microbial, pyrogenic) in and around this region are not considered when attributing the isotopic signature solely to ONG sources. A more comprehensive discussion of these sources would strengthen the conclusion that the primary contributor is the ONG sector.
• The paper lacks a thorough discussion and comparison with previous studies. Several key papers in this field are not cited, limiting the depth and impact of the findings.

I recommend major revisions to improve the clarity, and completeness of the manuscript. Given the scientific relevance of the study, it aligns well with the journal’s scope and should be considered for publication, provided that all suggested revisions are implemented.

A few specific comments:

• There should be space before the references in the text.
• There should be space between the numbers and units (for example -45.06 ‰).
• Adding the measurements from this study to the general source composition figure would enhance visualisation and provide a better context within a global framework.

L20: to the atmosphere from the Chinese oil and gas

L63: account

L66-L67: This sentence is not clear.

L69-L70: This sentence is not clear.

L78: isotopic composition?

L83-L85: Grammatically incorrect

L92: research

L120-121: Grammatically incorrect

L177: bags were lifted to the altitude

L183: sentence unclear

L220: rewrite this sentence

L227: ‘method of’ is a repetition

L378: the Keeling plot

L479 and L512: wrong reference formats

This manuscript presents new methane isotopic data for Chinese oil and gas infrastructure measured on a CRDS instrument. It concludes that it can distinguish between sources. Unfortunately, the data presented can only imply what the averaged fossil fuel signature might be. Overall the data shown are not convincing because: a) the emissions categories are not characterised at source, or close to individual emission points, and b) the source signature calculations mostly have very large errors and rely on a 1-point calibration of raw data.

To emphasize further comment a) above, it is important that you give a clear distinction between the facilities that are related to oil extraction with residual gas, as this gas component could be highly fractionated, or flared on site, with those that solely focus on gas upgrading and delivery to the network, where there is likely to be less isotopic variability. Your table of facilities does not make this clear. Combustion of fossil fuels will increase ¹³C in any emitted residual CH₄, so you need to be sure that this is not part of the activities.

To emphasize further comment b) above, the manuscript shows very limited understanding of the measurements and instrumentation. Calibration gases are measured by either metrology or by isotope ratio mass spectrometry and assigned a ratio. Each mass spectrometer behaves differently and so needs at least a 3-point calibration to slope correct the measured data across a range of isotopic signatures, say -75 to -20‰. The slopes for CRDS instruments show even greater calibration slope correction factors, so correcting data with one standard at -69‰ will not give the correct calibration at -10‰. Additionally, the authors refer to instrument accuracies, when they are actually showing precisions. Accuracy can only be assessed after calibration using at least 3 reference points, and applying the calibration equation to measurements of the calibration gases as unknowns. As the isotopic calibration will not change within error of measurement in the long-term it should still be possible to retrospectively provide a correction to the data by adding calibrants that are close to atmospheric background isotopic signature and with a more ¹³C-enriched signature.

The authors also discuss results of vertical profiles without showing the measurement precisions, but from the calibration gas precision it is clear that for some vertical profiles the whole of the variation seen is within the measurement precision of the instrument, so these results have no meaning. This is also true for more than half of the source signature graphs presented.

UAV sampling of air for laboratory isotopic measurement is not new, and there are papers at least back to 2016. For heights of a few hundred metres there is also the possibility to use AirCore sampling and get the full vertical profile rather than just at 3 or 4 heights.

There is no clear statement on what the Ground Open Area category is, or how it can be assigned an isotopic signature with any confidence. I am presuming that it is a mix of no sources, farmland sources and wetland sources, but this could give a signature anywhere between -70 and -55 ‰ depending on what the sources are and their relative proportions.

I also found that the selection of literature being reviewed was quite limited and focussed on a few geographical areas, missing a lot of key isotopic studies on methane.

Detailed Comments:

Abstract Line 33 – should have some explanation how a different isotopic signature can suggest a global overestimation.

Introduction

Lines 102-104 – Start a new paragraph for the UAV topic. UAV have been used to collect samples for isotopic analysis using air sampling bags much earlier than the studies that you mention.

Lines 104-106 – Don’t mix up UAV and large aircraft sampling. France et al. 2021 used a large aircraft, not a UAV.

Line 108 – studies concentrated in foreign countries. This statement depends on who is reading the text. You mean in countries outside of China.

Lines 111-114 – an isotopic signature is that which is calculated for the source. It cannot be used for the measurement of an atmospheric mixture. The signature will not change due to meteorology or sampling method. The measured values can be different due to sampling distance from the source and dispersion meaning that there is a larger component of the ambient air in the mixture, but the source signature will remain the same.

Lines 125-126 – what do you mean by Upper Atmosphere? You sampled at 200-300m. This is not upper atmosphere. That would be the mesosphere.

Method

Table 1 – needs better formatting to avoid splitting words between lines. The column on surrounding environment needs more detail for farmland. Does it contain rice growing, ruminants or both?

Figure 1 – needs a better picture to show how the bags and pump are connected to each other and to the UAV and to the sampling inlet. Perhaps drawing a schematic would be better than the top right picture.

Line 207 – as above, 200-300m is not high altitude.

Lines 213-215 – precision not accuracy; you can only get accuracy if you calibrate against isotopic standards of known composition across the known measurement range. The Picarro G2132 is a discontinued instrument before 2024.  Are these precisions quoted from Picarro for the G2132 or the newer replacement G2201i?

Line 216 - you cannot have a 1-point calibration for isotopes, as the isotopic calibration slope is very different for the CRDS between say -70 and -30‰, probably making a difference of about 1‰ for every 10‰ change in measured isotopic value compared to the slope defined by metrology and isotope ratio mass spectrometry. Therefore, if you calibrate at -70‰ you will be then incorrect by 4‰ at -30‰. You need at least 3 calibration points across your range of measured values.

Lines 257-258 – need to check some of the writing to avoid sentences like ‘Backward trajectories were used ….. to calculate ….. back trajectories’.

Results

Line 283 - this is not the range shown on box and whisker plots (1.7 ppm and below -50‰).

Figure 2 - how do you measure a CH₄ mixing ratio of 1.7 ppm that is well below the lowest measurement at any background site, using South Pole as the lowest?

Lines 302-321 - you cannot make statements about changes with altitude when they are smaller than the instrument measurement precision over the measurement period of 120 sec, which at near background mixing ratios (1.9-2.2 ppm) is about ±1‰ at best. Also, you should show the precisions on these values. Your isotopic data will be compared to those made by IRMS at <0.1‰, so the reader needs to believe that your measurements are actually showing real variations and not just measurement noise.

Lines 332-333 – you do not explain what is ground open area (also no explanation in the supplementary material), so what is it and how can you realistically assign an isotopic signature to it? Are we to presume that this is a mixture of no sources, natural wetland sources and farmland sources?

Discussion

Figure 5 – a problem with the low precision of CRDS for isotopes at close to background mixing ratios means that you could put any slope through most of these graphs, particularly as you have avoided to show any error bars on the measurements. Only those with trend errors of <2.5‰ are strongly correlated but again mostly by joining 2 clusters rather than a series of points along the line, an important factor to reduce source signature calculation errors.

Lines 422-424 – the statistical analysis in Figure S4 is not valid as the interpretation hinges on 1 point and without it there would be no correlation.

Lines 425-427 - presumably you measured upwind of each study area and collected a sample before sampling on site. If there was an influence from a wetland source for example you would see a higher CH₄ background and a depletion in ¹³C.

Lines 435-437 - you should specify that this statement is for fossil fuel extraction sites. This does not necessarily apply to other sources, and a big pipeline leak (500-1000 kg/hr) can come from a small point source.

Lines 471-485 - the isotopes of small % gas residue from an oil well are easily fractionated. Purely gas production wells tend to have much more homogenised signatures that are better correlated with the temperature that the gas is produced at, the hotter the gas production window the more enriched in ¹³C it is.

Figure 6 - you should say which studies you used for this compilation. There are far more studies outside of North America and China that you do not show here, so what you show is not representative. Additionally, ONG covers a lot of different source types. How can you be sure that you are comparing like with like?  So, it is not an overview figure. If you are using this data you should focus on oil and gas production areas (which includes Romania), but not include studies on refined gas distribution networks in European cities.

Lines 507-511 - you cannot pluck this statement out of the air with no explanation of how it is calculated (and even with the SI it is not very clear what are your defined parameters), and you should not compare gas as an oil field residual product, with gas production wells, or with a global average of -44‰ that includes coal, oil and gas sources with a range of 50‰ or more.

Lines 514-515 - what is the relevance of this sentence?  Yes, ponds have been overlooked, but most sources are not correct in the inventories, and there is much more work needed to reduce the uncertainties.

Line 521 - who says that it is an overestimation?  If you increase biogenic sources by 30% and fossil fuel sources by 10% the result is a decrease in global ¹³C.

Lines 524-525 - it does a lot more than just separate microbial and fossil sources and can distinguish a wide range of biogenic processes, such as between oxidation and reduction, between different feedstocks and diets. Microbial from fossil is just scratching the surface.

Line 529 - Unfortunately the data shown are not convincing, as the emissions categories are not characterised at source, or close to individual emission points.

Summary

Line 541 – Unfortunately the work does not distinguish between sources, it only implies what the averaged fossil fuel signature might be.

Line 556 - The global CH₄ isotope databases of Schweitzke, Sherwood and Menoud contain measurements from direct sampling of sources and measurement by isotope ratio mass spectrometry to high precision. Measurements of source signatures with large errors should not be included in any database that will be used for global and regional modelling.

References

Burnham et al. is not correctly formatted.

There are 9 references to web sites, all ending in the word ‘last’ and all incorrectly placed within the list.

Supplementary Data

S2 – how robust is the signature of -21.95‰? Signature is not based on correctly calibrated data so how do you know that it is typical? The precisions on many of the 11 station source calculations are very wide so these should not be used in averaging. Global fossil fuel includes a significant component of coal, whereas your ONG sites do not, so is this a good comparison?

S2 – you correctly use Saunois et al., 2024 here for the budget calculations, but in the main paper you use the older Saunois et al 2016 which has the budget to 2012.

Table S1 – basins are normally predominantly gas only, or oil with a bit of gas, and the gas only tend to have narrower isotopic ranges typical of mature gas. It would be useful to know what is the proportion of oil to gas extraction in these basins.

Table S2 – Time column - this is not a time and looks more like a date. Presume 13 April. Need to show the sampling height for pipeline and production areas.

Table S4 – I still have no idea what is Ground and how it can be assigned an isotopic signature.

Figure S4 - this statistical interpretation is not valid. Remove 1 data point and there is no correlation

Figure S5 - so why was the plume to the SW of site S7 not sampled for isotopic analysis, to get a more precise source signature? The plume could have been sampled at multiple points to give a spread of data on the Keeling plot and points above 10 ppm with much better CRDS precision.

Figure S8 – why is this included? The global trend is not part of the study and this trend has been reported in many other studies that have thoroughly analysed the reasons behind this trend.