The data were collected by the research teams of eight universities and research institutes: Utrecht University (UU), the Royal Holloway University of London (RHUL), the Laboratoire des Sciences du Climat et de l'Environnement (LSCE), Heidelberg University (UHEI), AGH University of Science and Technology (AGH), Lund University (LU), the University of Groningen (UG), and the Netherlands Organisation for Applied Scientific Research (TNO). They participated in several campaigns in the Netherlands, the United Kingdom, France, Germany, Poland, Sweden, Romania, and Turkey. Several other teams collaborated in two intensive campaigns: the CoMet

Carbon dioxide and Methane mission, May–June 2018

The mass spectrometry measurements were performed at two laboratories: the IMAU (Institute for Marine and Atmospheric research Utrecht) at UU, and at the Department of Earth Sciences at RHUL. Both laboratories use a CF–IRMS (continuous flow isotopic ratio mass spectrometry) system to measure δ13C, and also δ2H at IMAU. The system at IMAU was described by and the one at RHUL by . The reproducibility both groups can achieve is of 0.05 ‰ to 0.1 ‰ for δ13CCH4. At IMAU, δ2H measurements have a reproducibility lower than 2 ‰. For consistency of the results, the two laboratories measured a set of five cylinders that contained air with CH4 of different isotopic composition. The resulting differences in δ13CCH4 for each cylinder ranged between 0.02 ‰ and 0.04 ‰. They were within the analytical error reported by the two laboratories, so that the isotopic results obtained within the MEMO2 project are consistent across the laboratories. The inter-comparison exercise is presented in detail in a MEMO2 deliverable report, and publicly available .

The UHEI and LSCE groups performed isotopic measurements using CRDS instruments (G2201-i, Picarro inc., USA). Their measurement and calibration methods are described in and .

In the database, the method of isotopic measurements is specified by the “measurement type” parameter, as either “IRMS” or “CRDS”. The laboratory where the measurements were performed is specified in the column “measurement lab”.

The analytical parameters reported in the database are δ13CCH4 and δ2HCH4, which are defined as δX=RsampleRstandard-1, with R=13C12C for X=13C or R=2H1H for X=2H.

δ values are reported in per mille (‰), relative to the international standard materials Vienna Peedee Belemnite (VPDB) for δ13C, and Vienna Standard Mean Ocean Water (VSMOW) for δ2H.

The measurement results of δ13C and δ2H of CH4 are for ambient air, and not the sources themselves. There are different methods to derive the isotopic source signatures from the sampled CH4 enhancement signatures; the Keeling plot and Miller–Tans methods are commonly used mass balance approaches. The Keeling plot method is based on the assumption that the background is stable during the sampling period . The Miller–Tans method is also applicable when the condition of a stable background is not fulfilled . Because background samples were taken on each survey day and in the same region, the condition of stable background was usually fulfilled. showed that in this case, both methods lead to similar results within their uncertainty.

Both methods involve a linear regression model to fit the observed data. Different models were used: ordinary least squares (OLS) minimising the difference in the y axis coordinate, bivariate correlated errors and intrinsic scatter (BCES) , and orthogonal distance regression (ODR) . compared different regression methods to be applied in Keeling plots. The ODR method can induce a bias towards lower values, in the case the data points cover a relatively small range on the x axis. Therefore, the OLS and BCES methods were usually preferred to calculate the source signatures for this study.

All the mass balance and regression methods are statistically valid. We did not work towards a uniform procedure, to not modify the data that were processed by each lab. The different approaches are specified for each entry of the database by the parameters “mass balance approach” and “regression method”.

We used the same parameters as in the database of for non-fossil data. That is because our objectives concern only values for δ13C and δ2H of emitted CH4, and do not include measurements of other gases or isotope signatures that reported in the fossil fuel database. The variables of interest are listed in Table  and include the site description (country, region, group, category, and sub-category), and the δ13C and δ2H of CH4. There are two types of values:

Single measurement values, as from the characterisation of one emission event. Most fossil fuel data from are single measurements, and the entries in the EMID.

We found an additional number of 48 sources in the literature to complete the referred data listed in . Because we aim at reflecting the actual CH4 surface emissions to the atmosphere, we excluded studies that reported results from laboratory experiments, and of CH4 dissolved in water (i.e. in oceans, wetlands, and inland waters). We note that the search for data was biased because of the use of English language. The references we added concern published peer-reviewed articles, and to a lesser extent thesis and conference papers. We did not perform additional data quality assessment. The studies were performed from 1982 to 2021 in various laboratories in the world. The study locations do not overlap with the ones of the EMID or the literature gathered in , and we do not provide an analysis of potential temporal changes in the isotopic composition of the same source.

The extended global database including all literature data and the aggregated MEMO2 data consists of 13 313 and 4337 measurements of δ13C and δ2H, respectively, from 64 countries. The map in Fig.  shows the partitioning of the measurement data per country, and Table  the number of records per CH4 source. Table  contains statistics on the data from the EMID only, and the overall database including the EMID.

The number of measurements made in fossil fuel reservoirs and compiled in the database by is comparatively larger than from studies of other CH4 emission sources (Table ), and the number of measurements is not evenly spread geographically: significantly more measurements were made in North American and European countries, Australia, Brazil, and Japan. In Russia and China, there were relatively more measurements as well, but only for fossil fuel sources. Despite including the first few measurements reported from Africa and the Middle East , the data distribution remains unbalanced. Nevertheless, specific isotope signatures dependencies can be further analysed for the different source categories.

Fossil fuels. Fugitive emissions from fossil fuel reservoirs are highly variable not only on a large scale, but also from one basin to another, or even within the same basin . Therefore, CH4 isotopic composition from one basin cannot simply be upscaled to a country scale. Any new isotopic measurement from a production basin with large fugitive CH4 emissions brings relevant information. The recent measurements made in Romania, included in the EMID, illustrate well this heterogeneity .

pointed out the lack of data for a list of conventional oil and gas and coal production countries, in Africa, the Middle East, central and southern Asia, and South America. Previous estimates of global CH4 isotopic signatures from the exploitation of fossil fuels weighted the source signatures from one basin by its fuel production . Recent work suggests that fuel production is not a reliable proxy to estimate CH4 fugitive emissions . Thus, the most relevant sampling locations would be ideally related to estimated emission rates from top-down measurements, instead of production or bottom-up emission estimates. Unfortunately, these data are lacking in many cases. Recently, particularly large CH4 emissions were detected in central Asia , or measured in Mexico . Besides the new measurements in Romania, the EMID and additional literature we added to the global isotope database does not address the geographical representation issue.

Modern microbial. The isotopic signatures of CH4 from modern microbial sources (mainly wetlands, ruminants, waste degradation, rice paddies, and termites) are largely dependent on environmental parameters such as the type of substrate and other ecosystem conditions. Figures  and show that our new data confirm the trends previously observed: the δ13C sensitivity to C3 or C4 plants in ruminant diet , to wetland latitudes (δ13C depletion in polar regions because of less oxidation and the absence of C4 plants) , and the δ2H dependency on δ2HH2O of precipitation, and ultimately on the latitude (established for freshwater emissions) . Based on the correlation with the plant metabolism (C3 or C4), δ13CCH4 from wetlands could be mapped on a global scale . also suggested a spatial extrapolation of wetland δ2HCH4 using δ2HH2O data, which can be interesting for under-sampled locations, for example in the Southern Hemisphere. However, a certain variability will always remain because of the influence of other parameters such as the dominant methanogenic pathway (acetate fermentation or CO2 reduction) , or the δ13C composition of the organic matter substrate .

Biomass burning. Similarly to microbial degradation, the product of biomass burning is influenced by the plant constituents. CH4 produced from the burning of C3 or C4 plants can be distinguished based on the δ13CCH4 values e.g.. Higher δ13C signatures are measured when the burned plants are mostly C4 plants, and the δ13CCH4 is lower for C3 plants. This trend is clearly visible in the CH4 isotope dataset, and is shown in Fig. . The δ2HCH4 values are expected to depend on the δ2H of local precipitations , but more measurements are needed to support this hypothesis.

Statistical information on the CH4 isotopic signatures in the complete extended database are presented in Table . Figure  shows the distribution frequency of isotope signatures for the source categories that represent the largest reported emissions . The categories agriculture, waste, wetlands, and partly other natural are all of modern microbial origin, mostly from acetate fermentation . The different categories within microbial processes generally overlap (Fig. ). Some differences can however be observed, such as the wetlands mean δ13CCH4 being lower in the EMID than globally (-73.6±2.27 ‰ compared to -63.3±0.79 ‰), because the European samples were taken at relatively high latitudes (Sect. ). Table  also shows that waste sources present more enriched δ13CCH4 values than other modern microbial sources. This difference is particularly visible in the EMID, where a relatively large number of sites from waste-related sources were sampled. As mentioned in Sect. , additional parameters control the isotopic signature of the emitted CH4, such as the type of substrate, the presence of oxygen, or secondary (e.g. oxidation) processes. The minimum waste δ13C signature of -73.9 ‰ is comparable to the low values of other microbial sources, which supports the hypothesis of a larger influence of secondary processes in waste degradation relative to other microbial CH4 formation. We recommend to separate the waste category from the other microbial sources to minimise the uncertainty in the assigned isotopic signature, at least for δ13C. The range of δ2H signatures from waste sources is larger than of the other modern microbial sources, but the average δ2H from the different microbial sources are similar. One can see that δ2H is not systematically correlated with δ13C, and δ2H can also vary with other parameters such as the isotopic composition of water in the substrate. The δ2H signatures for waste are based on less measurements compared to δ13C (42 % of all measured waste sources included δ2H signatures). The relation between δ2HCH4 from wetlands and the δ2HH2O from precipitation has been established previously . We also know that the fractionation factors derived for CH4 microbial oxidation are much larger for δ2H than for δ13C . Nevertheless, further δ2H measurements are required to better define the isotopic dependencies to secondary processes.

In , the pyrogenic category only contained biomass burning data, and the binary distribution clearly illustrates the difference between C3 and C4 plants in terms of δ13CCH4 signatures: the averages in the global database are -28.4±0.65 ‰ and -18.0±1.9 ‰, for C3 and C4 plants, respectively. The additional biomass burning data we added from published literature confirm the dependency of δ13CCH4 on the plant metabolism (Fig. ). We also added pyrogenic data from fuel combustion (burning of fossil fuel) from both our measurements and the literature. The resulting distribution of the δ13C data is smoother than in (Fig. ), because the δ13CCH4 from fossil fuel burning does not show a clear distinction between C3/C4 plant metabolisms. δ2HCH4 isotopic signatures from pyrogenic sources cover a wide range of values, and overlap with the ones of fossil fuels. δ2H signatures allow to clearly distinguish between biomass and fuel combustion (Table ), but this is based on a very low number of measurements. Further analysis including data on δ2HH2O could help to parameterise the biomass burning δ2HCH4 in more detail , similar to the above mentioned relation between δ2HCH4 and δ2HH2O .

Fugitive CH4 emissions from fossil fuel source locations present a wide range of isotopic signatures: δ13C from -77.4 ‰ to -18.9 ‰, and δ2H from -349 ‰ to -101 ‰ (Table ). The average signatures of all fugitive CH4 emissions from the exploitation of fossil fuels (excluding seeps) in the EMID were δ13C=-44.6±0.4 ‰ (n=452) and δ2H=-182±2.4 ‰ (n=259), which compares well with the global average of −44.8±0.1 ‰ (n=8128) calculated in . Regarding the present updated global database, the weighted averages were δ13C =-46.6±1.8 ‰ and δ2H =-192±7.5 ‰, weighted by the relative emissions from conventional and coal fuels production worldwide.

Relative weights of 0.66 for conventional fuels (oil and natural gas) and 0.34 for coal. Emission data from .