Introduction
Isotope ratios of CH4 in the present and the past atmosphere (e.g. from
ice cores) are a powerful tool to study the biogeochemical processes that
cause the variation of CH4 in the atmosphere (Stevens and Rust, 1982;
Quay et al., 1991, 1999; Lowe et al., 1994; Sapart et al., 2012; Möller
et al., 2013; Sperlich et al., 2015; Schaefer et al., 2016). Recently, two
conflicting publications highlighted (i) the interpretative power when data
sets from multiple laboratories are combined for spatiotemporal analysis of
CH4 isotope ratios
(Kai et al., 2011) and (ii) the pitfalls when differences due
to laboratory offsets are misinterpreted as spatial variability of CH4
sources (Levin et al., 2012). Levin et al. (2012) identified
calibration offsets between three laboratories by comparing their long-term
observations in Antarctic background air, where the δ13C of
CH4 is assumed to be free of spatial gradients. However, this technique
is a temporary work-around that excludes the use of data sets from
laboratories without a history of observations in Antarctica or a traceable
link to Antarctic observations. This dilemma could be solved if suitable
reference materials (RMs) were available to all laboratories that measure
isotope ratios of atmospheric CH4.
Certified reference materials (CRMs) are provided by the IAEA, NIST and
others for many analytes. The lack of CRMs for CH4 isotope ratios has
long been recognised in the literature, ranging from pioneering papers (e.g.
Craig, 1953; Schiegl and Vogel, 1970) to recent publications
on analytical systems to measure isotope ratios in atmospheric CH4
(e.g. Sapart et al., 2011; Sperlich et al., 2013; Bock et al.,
2014; Tokida et al., 2014; Eyer et al., 2015) as well
as papers that present and interpret such data (e.g. Levin et al., 2012;
Sapart et al., 2013; Schaefer et al., 2016). In the absence of CRMs for
isotope ratios of CH4, many laboratories have developed methods to
calibrate purified CH4 against CRMs that were available as a
“second-best solution”, thereby accepting the shortcoming that those CRMs
comprised of different physicochemical properties and are therefore not ideal
(IAEA, 2003). For example, δ13C–CH4 calibrations were made
against NBS 20 (limestone) and NBS 21 (graphite) by Stevens and Rust (1982),
against NBS 16 (CO2) and NBS 20 (limestone) by Quay et al. (1991),
against IAEA-CO-9 (Barium carbonate) by Lowe et al. (1994), against NBS 19
(limestone) by Quay et al. (1999) and against RM 8563 (CO2) by Sperlich
et al. (2012). Dumke et al. (1989) calibrated against the natural gas
mixtures NGS 1, NGS 2 and NGS 3, which were not of the highest purity level
with 81, 53 and 99 % CH4 respectively (e.g. IAEA, 2003; Brand et
al., 2014). It is furthermore important to understand the variation of
uncertainties of the applied CRMs, ranging from assigned values of
0.00 ‰ (NBS 19, the only primary measurement standard for VPDB) up
to 0.56 ‰ (NGS 2)
(Brand et al., 2014). The situation becomes even more complicated because the
δ13C values of some of the applied CRMs were revised and
changed by as much as 0.4 ‰ over time (e.g. NBS 21;
Brand et al., 2014). As a consequence, this would require the adjustment
of dependent δ13C–CH4 data. The use of different
calibration methods, CRMs and the change of their assigned δ13C
values have undoubtedly contributed to calibration offsets between
laboratories. This fact highlights the importance that applied CRMs and
their δ13C values are reported in the metadata of the
measurement results and that their uncertainty is included in the
uncertainty budget of the measurements. Fortunately the situation is more
homogenous for δ2H–CH4 calibrations, which were only made
against CRM waters, such as VSMOW2, SLAP2 or their precursors (e.g. Schiegl
and Vogel, 1970; Dumke et al., 1989; Quay et al.,
1999; Sperlich et al., 2012). Brand et al. (2014)
provide a comprehensive overview on the variation of δ2H–H2O values and associated uncertainties. Another common method
for laboratories to anchor CH4 measurements to the VPDB or VSMOW
isotope scales is to get their working standard (WS) calibrated by an
external laboratory (e.g. Behrens et al., 2008; Brass and Röckmann,
2010; Bock et al., 2014; Schmitt et
al., 2014; Rella et al., 2015; Brand et al., 2016). It
is important to keep in mind that propagating isotope scales between
laboratories also requires inclusion and propagation of the uncertainty of
the respective isotope scale anchor.
In summary, the absence of unique CRMs for δ2H–CH4 and
δ13C–CH4 led to a diversity of calibration trajectories.
Significant calibration offsets between laboratories on the order of
0.05–0.09 ‰ for δ13C–CH4 were
identified through co-located measurements by Levin et al. (2012) and
Schaefer et al. (2016), while Bock et al. (2014) reported
laboratory offsets of up to 15 ‰ for δ2H–CH4. Even though inter-laboratory differences can be
established experimentally, e.g. by co-located measurements or regular
round robins, such comparisons are not intended to re-define local scale
anchors to the VPDB and VSMOW isotope scales (WMO, 2014) and can
therefore not replace a unifying scale anchor.
Calibration hierarchy to produce synthetic CH4-in-air standards
including links of the traceability chain. The long, central arrow shows that
the primary CH4 gases were directly calibrated against CRMs for
δ13C but not for δ2H. The uncertainty (U) associated
with each calibration hierarchy level is indicated by indices that are
described in Sect. 2.4.
Until recently, a comparable problem existed for observations of isotope
ratios in atmospheric CO2. Ghosh et al. (2005) established a
method to produce synthetic CO2-in-air standards, comprising of
isotopically calibrated CO2 and CO2-free air. The concept of these
CO2-in-air standards is to provide a matrix reference material (m-RM),
which is defined as RM that is mixed with matrix material to match the
composition of the samples (IAEA, 2003). Since 2005, the ISOLAB of the
Max Planck Institute for Biogeochemistry (MPI-BGC) in Jena, Germany,
distributes a suite of m-RMs, known as JRAS (Jena
Reference Air Set), which is accepted
as an isotope scale anchor by the community (WMO, 2012). Calibrating
against the JRAS reduces laboratory offsets and has proven a successful
method to reach and maintain the compatibility goal for isotope ratios in
atmospheric CO2 (Wendeberg et al., 2013).
This paper describes an analogue method to produce synthetic CH4-in-air
standards for δ2H–CH4 and δ13C–CH4,
which we refer to as JRAS-M16 (short for JRAS-Methane
2016). We present new methods to calibrate a suite of
isotopically different CH4 gases, which span over a large isotopic
range. We calibrate two CH4 gases for δ2H and δ13C and compare our results to independent calibrations made at a
partnering laboratory to demonstrate the comparability of our new methods,
thereby fulfilling the requirement to use two independent analytical methods
during the development of quality control materials (QCMs) when CRMs are not
available (IAEA, 2003). We produce synthetic CH4-in-air standards by
diluting aliquots of calibrated CH4 with CH4-free synthetic air
and include the full traceability chain in the uncertainty budget.
Calibrated δ2H–CH4 and δ13C–CH4 values
in our synthetic CH4-in-air standards bracket tropospheric values and
enable two-point calibrations to account for scale compression effects
(Coplen et al., 2006a). Our synthetic CH4-in-air standards can
be tested by other laboratories in the community; alternatively, compressed
air cylinders from other laboratories can be calibrated at MPI-BGC. Our
long-term strategy is to establish JRAS-M16 as m-RM for δ2H–CH4 and δ13C–CH4 in the future. We hope that our
efforts help the community to reach the scale anchor compatibility goals of
1 and 0.02 ‰ for δ2H–CH4 and δ13C–CH4 respectively (WMO,
2014).
Configuration of manual the two-position 10-port valve with two
1 mL sample loops shown in grey dashed box and TC/EA–IRMS system for
δ2H–CH4 calibration. The TC/EA–IRMS reactor
(displayed as in Gehre et al., 2004) is fed either by the sample line from
the 10-port valve or by the syringe via autosampler (not shown). The size of
components is chosen to increase clarity.
Materials and methods
Throughout this paper, we use the terminology of “calibration” and
“measurement” with different intentions. We use calibration when
samples are repeatedly compared against measurement standards of the highest
possible hierarchy level (possible hierarchy levels include CRMs and WSs)
in order to determine the isotopic composition of the analyte under
consideration of the full traceability chain. In contrast, we use the
measurement term when the analysis is not necessarily based on
measurement standards of highest possible hierarchy level, when the
achievable uncertainty of the analysis is not of primary importance or when
the uncertainty does not necessarily include the full traceability chain.
For example, we use the measurement term for the experiments to
establish the dependence of isotope ratios in the analyte on reactor
temperatures of the analytical system.
The aim of our method is to calibrate and prepare synthetic CH4-in-air
standards, as outlined in the flow diagram of Fig. 1. Therefore, we
calibrate two pure CH4 gases for their δ2H–CH4 and
δ13C–CH4 isotope ratios against CRMs and WSs, where the
latter are of comparable chemical composition to the former. We refer to
these two CH4 gases as primary CH4 gases. The primary CH4
gases are then used to calibrate a suite of pure CH4 gases, which we
refer to as secondary CH4 gases. The analytical methods we developed
for δ2H–CH4 and δ13C–CH4 calibrations
are based on well-established IRMS methods, thereby complying with the
requirements to use established analytical systems for the production of
QCMs when CRMs are not available (IAEA, 2003). Once calibrated, aliquots of
both primary and secondary CH4 gases are diluted with CH4-free air
to atmospheric CH4 mole fractions. We analyse the resulting synthetic
CH4-in-air standards on a new analytical system that is designed to
analyse atmospheric samples, thereby complying with the principle of
identical treatment (PIT; Werner and Brand, 2001) during the
analysis of the synthetic CH4-in-air standards. This enables us to
determine the calibration difference between JRAS-M16 and the hitherto
adopted method to reference δ2H–CH4 and δ13C–CH4 in atmospheric samples to the VSMOW and VPDB scales
respectively. This difference represents the laboratory specific correction
that has to be applied to anchor all measurements from MPI-BGC to the new
JRAS-M16 scale.
Gases, reference materials and hierarchy levels of calibrations
Our study is based on a suite of CH4 gases that differ in their
methanogenic origin and therefore in their isotopic composition. We identify
our CH4 gases by names as shown in Table 1. “Biogenic” and “Fossil”
have been calibrated at the Centre for Ice and Climate (CIC), which is a
department of the Niels Bohr Institute at the University in Copenhagen,
Denmark (Sperlich et al., 2012). These gases allow testing and evaluating the
performance of the analytical systems at MPI-BGC with independent methods,
which is a required control mechanism for the development of
QCMs when CRMs are not available
(IAEA, 2003). Six other CH4 gases were purchased from suppliers of
commercial gases or laboratory equipment (Air-Liquide, Westfalen AG, Linde,
Messer, Campro Scientific) and were used as purchased or as mixtures thereof.
The purity level of all our CH4 gases is ≥ 99.995 %. Our goals
were to produce (i) a suite of CH4 gases that encompasses the isotopic
composition of tropospheric CH4, and (ii) CH4 gases that closely
match the isotopic composition of tropospheric CH4. For
δ2H–CH4 this was achieved by spiking fossil CH4 gases
with pure CH3D to yield “Martha-1”, “Martha-2” and “Mike-1”.
Mike-1 was then mixed with a fossil CH4 gas to produce “Mike-2” while
Martha-1 was spiked with pure CH3D to produce “Martha-2”. Martha-1 and
Mike-1 were thereby transitional CH4 mixtures.
We calibrated “Megan” and “Merlin” for δ2H–CH4 and
δ13C–CH4 as primary CH4 gases (Fig. 1) against CRMs
and WSs to the VSMOW and VPDB isotope scales respectively. Applied WSs are
identical or similar in chemical composition to available CRMs in most cases
(Table 2). All secondary CH4 gases were calibrated against the primary
CH4 gases and are therefore of lower hierarchy level in the calibration
scheme (Fig. 1). Megan was used as primary CH4 gas for all
initial experiments and our first calibrations of secondary CH4 gases,
until it was accidentally vented to ambient in March 2015. In order to
compensate for the loss, we calibrated Merlin against CRMs and WSs as
primary CH4 in replacement of Megan.
Gases used for this study. Note that Mike-1 and Martha-1 were
transitional CH4 mixtures and do not exist anymore.
Cylinder
Gas name
volume (L)
Pressure (bar)
Function in study
CH4 source
Gas supplier
Megan
10
–
first primary CH4 (lost)
fossil CH4
Air Liquide, Germany
Merlin
10
190
second primary CH4 (replacement of Megan)
fossil CH4
Air Liquide, Germany
Mike-1
–
–
secondary CH4
MPI mixture
MPI-BGC
Mike-2
5
45
secondary CH4
MPI mixture
MPI-BGC
Merkur
2
100
secondary CH4
fossil CH4
Messer Griesheim, Germany
Merida
10
175
secondary CH4
unknown
Westfalen AG, Germany
Martha-1
–
–
secondary CH4
MPI mixture
MPI-BGC
Martha-2
10
165
secondary CH4
MPI mixture
MPI-BGC
Minion
3
150
secondary CH4
unknown
Messer Griesheim, Germany
Melly
50
193
secondary CH4
unknown
Westfalen AG, Germany
δ2H-spike gas
0.4
2.5
CH3D spiking gas
Campro Scientific, Germany
Fossil
30
2
secondary CH4 and comparison
fossil CH4
Air Liquide, Denmark
Biogenic
30
2
secondary CH4 and comparison
biogas plant
Biogas Plant, Germany
synthetic air
50
200
Synthetic air matrix
Linde, Germany
Krypton
2
200
synthetic air matrix
Westfalen AG, Germany
Carina-1
50
200
working standard and scale comparison
Jena air
MPI-BGC
Carina-2
50
200
working standard and scale comparison
Jena air
MPI-BGC
Measurement standards used in this study. “CRM” and “WS”
identify certified reference material and in-house working standards
respectively. The uncertainties of the δ2H and δ13C data from MPI-BGC correspond to the 95 % confidence limit of
the error of the mean. We include the uncertainty estimate that the IAEA
recently suggested for LSVEC. Publications and additional comments related
to the standards are listed in the last column.
Name
Material
CRM/WS
δ2H [‰]
δ13C [‰]
Source
Reference/comment
VSMOW2
H2O
CRM
0 ± 0.3
–
IAEA
Gröning et al. (2007), Brand et al. (2014)
SLAP2
H2O
CRM
-427.5 ± 0.3
–
IAEA
Gröning et al. (2007), Brand et al. (2014)
GISP
H2O
CRM
-189.7 ± 0.9
IAEA
Brand et al. (2014)
NBS 19
CaCO3
CRM
+1.95 ± 0.00
IAEA
Brand et al. (2014), exhausted
LSVEC
Li2CO3
CRM
–
-46.6 ± 0.15
IAEA
Coplen et al. (2006b), Qi et al. (2016), Schimmelmann et al. (2016)
CO-9
BaCO3
CRM
–
-47.32 ± 0.06
IAEA
Coplen et al. (2006b)
RM 8563
CO2
CRM
-41.59 ± 0.06
Coplen et al. (2006b), exhausted
WWW-J1
H2O
WS
-67.0 ± 0.4
MPI-BGC
–
BGP-J1
H2O
WS
-187.1 ± 0.6
MPI-BGC
–
MAR-J1
CaCO3
WS
+1.96 ± 0.01
MPI-BGC
Brand et al. (2009b)
ALI-J3
Acetanilide
WS
-30.06 ± 0.05
MPI-BGC
–
Cecily
CO2
WS
-3.84 ± 0.015
MPI-BGC
–
Carina-1
Jena air
WS
-82.7 ± 4.0
-47.61 ± 0.09
MPI-BGC
calibration (T. Röckmann, personal communication, 2013)
Carina-2
Jena air
WS
-85.5 ± 4.0
-47.62 ± 0.12
MPI-BGC
calibration (T. Röckmann, personal communication, 2013)
Referencing pure CH4 for δ2H against VSMOW/SLAP and
against other pure CH4 gases
We use a high-temperature conversion elemental analyser (TC/EA) coupled to an
isotope ratio mass spectrometer (IRMS; Delta Plus XL, Thermo Finnigan,
Bremen, Germany) via an open split (ConFlo III, Thermo Finnigan, Bremen,
Germany). The system at MPI-BGC is operated for δ2H–H2O and
δ18O–H2O analysis with high precision and negligible
systematic errors since more than a decade (Gehre et al., 2004; Brand et al.,
2009a) and is depicted in Fig. 2. Because TC/EA–IRMS systems are also
used for δ2H analysis in hydrocarbons (e.g. Hilkert et al., 1999;
Schimmelmann et al., 2016), this method is particularly suitable to calibrate
δ2H–CH4 against reference H2O (CRM and WS, Table 2).
Therefore, we inject CH4 and H2O samples through an externally
heated septum (kept at 130 ∘C) into the glassy carbon reactor of the
TC/EA–IRMS (kept at 1450 ∘C), where both species are
converted to H2 (+ carbon or CO). A helium carrier gas transports the
sample gases from the high-temperature reactor through a gas chromatographic
(GC) column (1/4 in. OD, 60 cm length, 5-Å
zeolite, 75 ∘C) and into the open split. CH4 injections are
made with a two position 10-port valve (VICI, USA), which is configured as
depicted in Fig. 2. A helium stream of 15 mL min-1 carries CH4
samples from the 1 mL sample loops into the TC/EA reactor. Typical CH4
flow rates range between 2 and 3 mL min-1. For the calibrations of
primary CH4 gases, the two sample loops are fed by the same CH4 gas
(connecting vent 1 and CH4 port 2). The sample loops are fed by two
different gases for the calibration of secondary against primary CH4
gases (Table 1). While CH4 gases are injected manually, H2O is
introduced via autosampler.
It is recommended to measure samples against standards with identical
material-specific properties (PIT; Werner and Brand, 2001).
Under such conditions, measurement artefacts are likely to cancel when, for
example, H2O samples are calibrated against H2O standards.
However, great care has to be taken when chemically identical or similar
CRMs are not available so that sample and standard comprise materials with
different chemical properties, which is the case when calibrating CH4
against H2O. Calibration errors may arise when only one or both
materials are fractionated during analysis, where the latter is likely to
occur with different fractionation factors.
We performed a range of experiments to test for systematic errors during
H2O and CH4 analysis. (i) System memory occurs during the isotopic
analysis of H2O due to adhesion of H2O onto internal surfaces.
System memory is sufficiently minimised by repeated H2O injections and
rejection of the first sample in every sequence. Remaining memory effects
are corrected for in the evaluation routine as shown by Gehre et al. (2004). System memory is not created by CH4 injections but
some δ2H–CH4 analyses may be affected by desorption of
H2O, stemming from previous injections. (ii) We observe a systematic effect of the septum
temperature on the resulting δ2H–H2O and operate the
system with a septum temperature of 130 ∘C, where δ2H–H2O was found stable. δ2H–CH4 analysis is
not affected by septum temperature. (iii) We experimentally optimised the TC/EA
reactor temperature and found highest H2 yields, quantitative
conversion and hence smallest isotopic fractionation at 1450 ∘C
during both δ2H–H2O and δ2H–CH4
analysis. Appendix A describes these experiments in greater detail.
The introduction of H2 samples into the ion source of an IRMS leads to
the formation of H3+ ions that are registered on the HD+
detector, which is accounted for by the so called “H3-factor
correction” (Friedman, 1953; Sessions et al.,
2001). The H3-factor correction is experimentally
determined and assumed to be constant until re-determined. Determining the
H3-factor correction is part of the daily preparation routine at
MPI-BGC and shows only minor variation with time. Theoretically, the
H3+ formation could be dynamic during the experimental period with
unknown variability. We matched the H2 peak heights resulting from both
CH4 and H2O injections around 5.5 ± 0.5 V in order to
minimise the impact of imperfect H3-factor correction. Peak widths
ranged around 45 and 60 s for H2O- and CH4-derived H2
peaks respectively. A typical chromatogram of the δ2H–CH4
calibration including details on peak shape and background is shown in Fig. 3. The similarity between CH4-derived and the H2O-derived H2
peaks allows the use of the standard integration software (ISODAT, Thermo
Finnigan, Bremen, Germany).
Megan and Merlin (Table 1) were calibrated in three independent
sequences during 3 days against the in-house working standards
“WWW-J1” and “BGP-J1” with a wide δ2H range from -67.0 to
-187.1 ‰ (Table 2). WWW-J1 and BGP-J1 are
independently calibrated against international reference waters VSMOW2 and
SLAP2 (Table 2). Other CH4 gases were initially also measured against
working standards (WWW-J1 and BGP-J1) but were finally calibrated against
Megan or Merlin, which were co-analysed in the same measurement
sequence in a one-point calibration.
Chromatograms of δ2H–CH4 calibration sequences using
TC/EA–IRMS with traces of m/z 2 and m/z 3 shown in black and blue
respectively. The bottom panel shows an example of an entire calibration
sequence which begins with three square-shaped peaks of pure H2,
followed by alternations of three to four H2O- and three to four
CH4-derived H2 peaks before the sequence ends with another three
square-shaped peaks of pure H2. The top left panel enlarges H2
peaks from H2O (peak no. 6–7) and CH4 (peak no. 8–9) injections
respectively. A zoom into baseline details of H2O-derived peak no. 7 and
CH4-derived peak no. 8 is shown in the top right panel. Red lines
indicate the sections used for peak integration (weak widths are 43 and 59 s
for H2O- and CH4-derived H2 peaks respectively) by the IRMS
software.
Referencing pure CH4 for δ13C against LSVEC/MAR-J1
and against other pure CH4 gases
We calibrated δ13C–CH4 in pure CH4 gases after
conversion to CO2 using an elemental analyser (EA 1100, CE, Rodano,
Italy) coupled to an IRMS (Delta Plus, Thermo Finnigan, Bremen, Germany) via
open split (ConFlo III, Thermo Finnigan, Bremen, Germany). This system is
routinely used for the analysis of 13C and 15N in samples with
solid or liquid matrices (Werner et al., 1999; Brooks et al.,
2003). We fitted a 1/16 in. tube of 70 / 30 % Cu / Ni alloy to the EA
and used the previously described 10-port valve to inject the CH4
samples into the EA with a 10 mL min-1 helium flow (Fig. 4).
The 10-port valve for manual CH4 injections is coupled to the
EA–IRMS system through custom-made gas inlet into combustion (oxidation)
unit for δ13C–CH4 calibration. The proportions of
illustrated components are chosen to increase clarity.
The plumbing of the system is designed so that gaseous CH4 and solid
CRMs/WSs are applied to the same location inside the combustion reactor of
the EA. All samples are combusted at a reactor temperature of
1020 ∘C (Werner et al., 1999) and experience identical
analytical treatment thereafter. Following the combustion, each sample
passes through a reduction reactor filled with elemental copper, which is
kept at 650 ∘C to remove excess O2 and to reduce NOx if
present. The sample is dried by passing through a Nafion™ membrane
(Perma Pure LLC, Toms River, NJ, USA; not shown in Fig. 4) and a
Mg(ClO4)2 trap before it enters the GC column (3 m, 1/4 in.; Porapak
PQS, CE instruments) held at 80 ∘C. Thereafter, the sample enters
the IRMS through the open split.
Measurement sequences to calibrate primary CH4 gases to the VPDB
isotope scale are created by alternating blocks of manual CH4
injections and CRM/WS (Table 2) applications via autosampler. We applied one
WS and one CRM (LSVEC) to calibrate the primary CH4 gases in a two-point calibration. While MAR-J1 was used as WS in most experiments, ALI-J1
was used once, during a calibration of Merlin. Megan and Merlin
were each calibrated on 3 different days to determine the external
reproducibility of the δ13C results. Chromatograms resulting
from CH4 and from carbonate analyses using EA–IRMS are displayed in
Fig. 5 and show very similar peak shapes for CH4 and carbonates.
Typical m/z 44 amplitudes and peak widths were ∼ 7.4 ± 0.2 V and 101 ± 1 s for both materials respectively. We connected a
primary CH4 and a secondary CH4 gas to the 10-port valve to
calibrate the secondary CH4 gases (Table 1) for δ13C in a
one-point calibration. All measurement results were corrected for scale
compression based on the method suggested in Verkouteren and Klinedinst (2004), using an empirical, mass spectrometer specific correction
factor of 1.0056.
Chromatograms of δ13C–CH4 calibrations using
EA–IRMS with traces for m/z 44, 45 and 46 in green, brown and black
respectively. Bottom panels show complete chromatograms of CH4 and
Li2CO3 analyses while the two top panels zoom into the baseline of
the traces. The first three square-shaped peaks stem from injections of a
pure CO2 WS while the more Gaussian-shaped peaks result from CH4- and Li2CO3-derived CO2 analysis. The two red lines indicate
the sections that the IRMS software uses for peak integration (CO2 peak
widths are 101 and 100 s for CH4 and Li2CO3 analysis
respectively).
Measurement uncertainty and error propagation
The fully propagated uncertainty for the primary CH4 gases
(UpCH4-tot) is calculated as
UpCH4-tot=uCRM2+uWS2+upCH42,
where uCRM, uWS and upCH4 indicate the uncertainty of the
CRM, the applied working standards and the respective primary CH4
gas respectively. Both uWS and upCH4 are calculated as the
standard error of the mean of all measurements, multiplied by t, Student's
factor for a 95 % confidence limit to account for the limited number of
measurements.
The uncertainty for the secondary CH4 gases (UsCH4-tot) is then
calculated as
UsCH4-tot=UpCH4-tot2+usCH42,
where usCH4 is the standard error of the mean of all measurements of
the respective secondary CH4 gas, multiplied by t, Student's factor for
a 95 % confidence limit. Therefore, UpCH4-tot and UsCH4-tot
indicate the fully propagated uncertainty onto the VPDB or VSMOW isotope
scales, representing the traceability chain.
Note that the isotopic composition of LSVEC (Table 2) was recently found to
show significant variability, most likely due to adhesion of H2O and
reaction with air-CO2 (e.g. Qi et al., 2016; Schimmelmann et al.,
2016). Until this problem is solved, the IAEA, one of the providers of
LSVEC, advised to increase the uncertainty of LSVEC, which was hitherto
assigned to 0.00 ‰. We follow the recommendation by S. Assonov (Sergey Assonov, IAEA, personal communication, 2016) and Schimmelmann et al. (2016)
and adopt an uncertainty of 0.15 ‰ for the
δ13C of LSVEC. Note that the new 0.15 ‰
uncertainty of LSVEC represents the largest single contributor to the total
uncertainty budget in our δ13C calibrations. As a consequence
we present the combined uncertainty of the full traceability chain in two
versions, the first being the hitherto adopted method using an uncertainty
of 0.00 ‰ for LSVEC and the second being the method with
uncertainty for LSVEC of 0.15 ‰.
Producing synthetic CH4-in-air standards from pure CH4 and
CH4-free air (JRAS-M16)
The MPI-BGC operates an analytical system (named ARAMIS) to dilute pure
CO2 with CO2-free air to atmospheric CO2 mole fraction
without isotopic fractionation (Ghosh et al., 2005). We use
ARAMIS to dilute an aliquot of primary or secondary CH4 with
CH4-free air to atmospheric CH4 mole fractions (∼ 2 ppm) in 5 L glass flasks with a final filling pressure of 1.8 bar absolute.
The produced synthetic CH4-in-air standards represent the JRAS-M16
reference gases. The CH4-free matrix air has been target-mixed from
ultra-pure constituents and contains N2, O2, N2O and Kr at
atmospheric levels, so that the composition of the produced CH4-in-air
standards is as close to ambient air as possible. Krypton was added to this
matrix air to account for the measurement artefact during GC–IRMS analysis
of CH4 for δ13C (Schmitt et al., 2013). A
blank analysis of the CH4-free air yielded a maximum CH4 blank of
0.5 ppb. Because such a CH4 blank is too small for accurate isotopic
analysis on our atmospheric system (Sect. 2.6 and Brand et al., 2016), we choose a mass-balance calculation to determine the
maximum blank effect in the synthetic CH4-in-air standards. Let us
assume δ2H–CH4 and δ13C–CH4 values for
the CH4 blank of -150 and -40 ‰, respectively, that are typical for fossil CH4.
Let us now mix this blank with target CH4 comprising the most depleted
δ2H–CH4 and δ13C–CH4 values we find in
our CH4 gas suite with δ2H of -320 ‰
and δ13C of -70 ‰. The mixing ratio of
blank:target CH4 is 1: 4000, which reflects the ratio within a synthetic
CH4-in-air mixture with 2 ppm CH4. The maximum blank contribution
in this extreme scenario would be 0.04 and
0.007 ‰ for δ2H–CH4 and δ13C–CH4, respectively, which is negligible in both cases.
Analytical systems to measure the isotopic composition of CH4 in
air at IMAU and MPI-BGC
IMAU: Brass and Röckmann (2010) and
Sapart et al. (2011) described the system for the analysis of both
δ2H and δ13C in atmospheric methane at IMAU.
CH4 is separated from the other air components by cryogenic traps and
gas chromatography before it is converted by either oxidation or pyrolysis
for IRMS analysis on CO2 or H2 respectively. For δ13C an additional GC column (PoraPlotQ, 12.5 m, 0.32 mm ID, Agilent,
the Netherlands) was added between the Nafion drying unit and the open split
interface in order to remove the interferences from Kr
(Schmitt et al., 2013).
MPI-BGC: a new system to measure δ2H–CH4 and
δ13C–CH4 in-air samples was recently developed at the
MPI-BGC and is described in greater detail in Brand et al. (2016).
The system at MPI-BGC is referred to as iSAAC, in abbreviation for integrated System for Analysis of
Atmospheric Constituents. iSAAC consists of a
16-port sample carousel to take two consecutive 100 mL aliquots of air from
a
glass flask or high-pressure cylinders for parallel analysis of δ2H–CH4 and δ13C–CH4, respectively, by
continuous-flow GC–IRMS. The two air samples are routed through two
identical but independent pre-concentration lines, one for the analysis of
δ2H–CH4 and one for δ13C–CH4. In each
line, CH4 is cryogenically separated from the main air constituents in
a Hayesep D-filled trap at -130 ∘C and cryo-focussed in a further
Hayesep D-filled trap at -110 ∘C. Each of the two analytical
lines is equipped with its own cooling compressor to avoid the use of
cryogenic liquids. The separated and cryo-focussed CH4 sample is
released into a GC column from where it is routed either through a pyrolysis
furnace (kept at 1400 ∘C) to convert the CH4 sample to
H2 for δ2H–CH4 analysis or through a combustion
furnace (kept at 1000 ∘C) to convert the CH4 sample to
CO2 for δ13C–CH4 analysis. A post-combustion GC
column separates the CH4-derived CO2 from Kr
(Schmitt et al., 2013). CH4-derived H2 and
CO2 samples are introduced via open splits into dedicated IRMS
instruments, one each for δ2H–CH4 and δ13C–CH4 analysis. iSAAC has been operational since 2012 to measure
air samples with a precision of 1.0 and 0.12 ‰ for δ2H–CH4 and δ13C–CH4 respectively. The precision is determined by the
performance chart method (Werner and Brand, 2001), determined
by the standard deviation (1σ) of all quality control standard
measurements, which has been analysed once in every measurement sequence
(Brand et al., 2016). The reproducibility of δ13C–CH4 analyses ranges around 0.06 ‰ over
the course of 1 day. All measurements on iSAAC so far have been allocated
to the VPDB and VSMOW scales using an in-house WS that was calibrated
against “Carina-1” (Table 1).
Histories to anchor δ2H–CH4 and δ13C–CH4 to the VSMOW and VPDB scales at IMAU and MPI-BGC
It is the intention of all laboratories analysing δ2H–CH4
and δ13C–CH4 to reference their samples relative to the
VSMOW and VPDB scales respectively. However, possible accuracy errors in
the laboratory specific scale anchors often result in inter-laboratory
offsets. In order to retrace the potential for calibration offsets between
IMAU, MPI-BGC and JRAS-M16, we describe the history of the scale anchors for
each laboratory.
IMAU: the calibration strategy at IMAU, including traceability
chain and long-term control, is different for δ2H–CH4 and
δ13C–CH4 (Brass and Röckmann,
2010). (i) Three synthetic gas mixtures with CH4 mole fractions of
∼ 9000 ppm were calibrated for δ2H–CH4 at
the Max Planck Institute for Chemistry (MPI-C) in Mainz, Germany, using a
tunable diode laser absorption spectrometer (TDLAS) technique. The TDLAS is
described by Bergamaschi et al. (1994) with a measurement precision for
δ2H–CH4 of 5.1 ‰ and an accuracy
estimate of similar magnitude. The accuracy estimate is based on a
comparison with the calibrations to the VSMOW scale by Dumke et al. (1989), which marks the origin of the isotope scale anchor for
δ2H–CH4 at IMAU. Aliquots of the gases from Bergamaschi et
al. (1994) were diluted with synthetic CH4-free air at IMAU to
yield reference gases (“Cal1”, “Cal2”, “Cal3”) with the δ2H–CH4 values initially assigned at MPI-C and atmospheric
CH4 levels. Improved measurement precision and inter-laboratory
comparisons lead to a δ2H–CH4 refinement in Cal1,
Cal2 and Cal3 with recent values of +21.1,
-19.0 and -164.9 ‰
respectively. Cal1, Cal2 and Cal3 represent the primary
reference gases for δ2H–CH4 at IMAU and were used to
calibrate the δ2H–CH4 in the working standard (“SiL”) to
the VSMOW scale. While Cal2 and Cal3 have become exhausted, Cal1
is still used in regular checks of the calibration scale, together with a
set of firn air samples (see ii) that are used for δ13C
calibration. (ii) IMAU's working gas SiL has also been calibrated for
δ13C–CH4. This was achieved by co-analysing SiL with a
suite of Antarctic firn gas samples, where the δ13C–CH4 of
the latter had been determined by two laboratories (MPI-C and the
Laboratoire de Géologie et Géophysique de l'Environnement (LGGE),
Grenoble, France), using two different techniques
(Bräunlich et al., 2001). The δ13C–CH4
scale anchors at LGGE and MPI-C are calibrated at MPI-C against a pure CO2
WS, which itself has been calibrated against NBS 19
(Bergamaschi et al., 2000), which represents the ultimate
link to the VPDB scale for the scale anchor at IMAU. Using that method, the
suite of firn gas samples was treated as a set of working standards to
calibrate SiL to the VPDB scale by propagation from MPI-C and LGGE to
IMAU. It is important to note that Brass and Röckmann (2010) highlighted that the firn gas itself is a set of
samples and not to be taken for a set of calibration standards. The
calibration strategy was revised during 2013 to account for the Kr
interference (Schmitt et al., 2013).
MPI-BGC: all measurements on iSAAC use a natural air WS that was
calibrated against Carina-1 at MPI-BGC. Carina-1 and Carina-2
are natural air samples that were calibrated for δ2H–CH4
and δ13C–CH4 at IMAU (Table 2), using the analytical setup
described by Brass and Röckmann (2010) and Sapart
et al. (2011). While the calibration results of Carina-1 and
Carina-2 from IMAU show excellent agreement in CH4 mole fractions
(both 1910 ppb), in δ13C–CH4 (within
0.01 ‰), their δ2H–CH4 values
differed by 2.8 ‰ (Table 2). Because both Carina
cylinders were filled at the MPI-BGC with Jena air on the same day within a
short period of time during stable meteorological conditions, and because
their δ13C–CH4 and CH4 mole fractions are in
excellent agreement, a true difference in δ2H–CH4 between
the two Carina cylinders seems unlikely. The magnitude of the δ2H–CH4 offset was smaller than the
former δ2H–CH4 measurement precision at IMAU of ±4 ‰ (Brass and Röckmann, 2010)
and was accepted as “agreement within measurement uncertainty” at the time. It
is important to note that Carina-1 and Carina-2 were each calibrated
on different days and in separate measurement sequences, which does not
enable a direct comparison of the two gases. Therefore, a systematic
calibration error in one of the two Carina gases is possible. In
contrast, the superior measurement precision of iSAAC for δ2H–CH4 of 1.0 ‰ can resolve a true δ2H–CH4 difference of 2.8 ‰. However, both
Carina-1 and Carina-2 appear indistinguishable in δ2H–CH4 on iSAAC, as determined during several direct comparisons
in independent measurement sequences. Therefore, the δ2H–CH4 offset between Carina-1 and Carina-2 must be due
to an artefact of the calibration at IMAU. Our experiments at MPI-BGC
indicate that the calibration of Carina-1 is indeed flawed. The choice
to use Carina-1 as scale anchor for all iSAAC measurements at MPI-BGC was
made arbitrarily, before it was known that it's calibration was impacted by
an artefact. In hindsight, Carina-2 would have been a better choice as
VSMOW scale anchor for δ2H–CH4 at MPI-BGC. This
calibration offset will be furthermore addressed a future comparison with
IMAU, where a new system has been developed with an improved precision in
for δ2H–CH4 (Röckmann et al.,
2016). All iSAAC measurements are anchored to the VSMOW and VPDB isotope
scales based on the described scale propagation from IMAU to MPI-BGC, until
JRAS-M16 is established as new m-RM for δ2H–CH4 and
δ13C–CH4 in air.
Comparison of the existing isotope scales at MPI-BGC with new, synthetic
CH4-in-air standards
The synthetic CH4-in-air standards produced in this study (Sect. 2.5)
were analysed at MPI-BGC using iSAAC (Sect. 2.6). In that, the synthetic
CH4-in-air standards are treated as unknown samples and their δ2H–CH4 and δ13C–CH4 values are determined using
Carina-1 as scale anchor (Sect. 2.7). We calculate the isotopic
difference (δiSAAC–δpure) between
the measurements on iSAAC and the calibrations of the pure CH4 gases
(Sects. 2.2 and 2.3), which indicates the correction to anchor the
measurements at MPI-BGC to JRAS-M16.
Comparison between CIC and MPI-BGC
Two CH4 gases, Biogenic and Fossil, were previously calibrated at CIC by
Sperlich et al. (2012), who analysed the CH4-derived CO2 for
δ13C–CH4 by dual-inlet IRMS and the CH4-derived H2O
for δ2H–CH4 by either cavity ring-down spectroscopy (CRDS) or
TC/EA–IRMS. Sperlich et al. (2012) presented the data with the
measurement reproducibility, calculated as the pooled standard deviation of
the measurements. Therefore, their uncertainty does not include the
uncertainties of the full traceability chain. Furthermore, a statistical provision that accounts for the small number of measurements has not been made by Sperlich et
al., (2012).
This imposes a hurdle in the comparison with data from MPI-BGC. Therefore, we
revise the uncertainty of the CIC data and calculate the full traceability
chain as described in Sect. 2.4. Furthermore, all δ13C measurements
from CIC are affected by a small offset of RM 8563 that has been reported by
Coplen et al. (2006b) and are therefore shifted by 0.03 ‰ towards
more depleted δ13C values. Moreover, the δ13C data
presented in Sperlich et al. (2012) have not been corrected for scale
compression. We are able to correct all CIC data for this effect, because the
scale compression factor of the instrument at CIC has been determined
(1.0025) at the time the study of Sperlich et al. (2012) was published.
Applying the scale compression correction shifts the δ13C–CH4
of Fossil and Biogenic by 0.01 and 0.05 ‰ towards more depleted
δ13C values respectively. The revised data and uncertainties from
CIC and the results from MPI-BGC for Biogenic and Fossil are shown in Table 4
for δ13C–CH4 and in Table 5 for δ2H–CH4.
Results of CH4 isotope calibrations. Gas names as used in main
text and their function as primary or secondary CH4 are shown in column
1 and 2 respectively. All uncertainty estimates include the full
traceability chain (Sect. 2.4). Note that we provide uncertainty estimates
for δ13C–CH4 without and with the uncertainty of
0.15 ‰ in LSVEC in column 6 and 7 respectively.
Martha-1 and Mike-1 were intermittent gases and used to produce Martha-2 and
Mike-2.
δ13C–CH4 [‰]
δ13C–CH4 [‰]
Gas name
Function
n (δ2H)
δ2H–CH4 [‰]
n (δ13C)
uLSVEC= ±0.00 ‰
uLSVEC= ±0.15 ‰
Megan
primary
116
-168.1 ± 0.7
15
-40.76 ± 0.04
-40.76 ± 0.16
Merlin
primary
51
-165.7 ± 0.7
32
-39.06 ± 0.02
-39.06 ± 0.15
Martha-1
secondary
15
-176.6 ± 0.8
10
-48.84 ± 0.07
-48.84 ± 0.17
Martha-2
secondary
9
+36.2±1.0
19
-48.92 ± 0.06
-48.92 ± 0.16
Mike-1
secondary
12
+44.5±0.9
8
-40.79 ± 0.09
-40.79 ± 0.17
Mike-2
secondary
15
-80.3 ± 0.5
13
-42.76 ± 0.05
-42.76 ± 0.16
Merida
secondary
12
-171.7 ± 0.9
13
-60.39 ± 0.09
-60.39 ± 0.18
Melly
secondary
19
-177.5 ± 0.7
15
-70.04 ± 0.07
-70.04 ± 0.17
Minion
secondary
12
-182.7 ± 0.8
15
-58.19 ± 0.05
-58.19 ± 0.16
Merkur
secondary
15
-195.8 ± 0.9
19
-43.05 ± 0.04
-43.03 ± 0.16
Fossil
secondary
15
-171.9 ± 0.9
16
-39.71 ± 0.08
-39.71 ± 0.17
Biogenic
secondary
25
-319.8 ± 0.8
10
-56.60 ± 0.07
-56.60 ± 0.17
We perform two comparisons between CIC and MPI-BGC. (i) The calibration
results for Fossil and Biogenic from CIC as published in Sperlich et
al. (2012) are compared to the calibrations at MPI-BGC using the methods
to calibrate pure CH4 gases for δ2H–CH4 and δ13C–CH4 as described in Sects. 2.2 and 2.3. (ii) We performed new
combustion experiments at CIC using Fossil and Biogenic and analysed
the resulting CO2 for δ13C at both CIC and MPI-BGC. These
combustion experiments were made in 2012 but after the publication of
Sperlich et al. (2012). Therefore, these experiments provide new data to
evaluate the method at CIC. Following the δ13C analyses at CIC,
the remaining CO2 gases were cryogenically transferred and flame sealed
in glass ampules for δ13C analysis at MPI-BGC. The δ13C analyses at MPI-BGC were made on “Cora”, a MAT 252 dual-inlet IRMS
(Thermo Finnigan, Bremen, Germany) that is used for δ13C and
δ18O analysis of CO2 in air or pure CO2 gases
(Brand et al., 2009b). Unfortunately, the comparison based on the new
combustion experiments made at CIC could not include δ2H–CH4 because the system was not capable to process CH4
samples large enough to provide sufficient amounts of H2O.
We use the indices CIC-old for experiments made at CIC and published by
Sperlich et al. (2012) and CIC-new for the new combustion
experiments at CIC. We use the index MPI-BGC∗ for the analysis at
MPI-BGC of CO2 samples that were combusted at CIC and MPI-BGC for
the calibrations of the two CH4 gases from CIC using the analytical
methods at MPI-BGC presented above (Sect. 2.2 and 2.3).
Discussion
Discussion on the experimental artefact elimination during δ2H–CH4 and δ13C–CH4 calibrations in primary and
secondary CH4 gases
We present δ2H and δ13C calibrations in pure
CH4 gases against CRMs, WSs and other CH4 gases. Samples and
reference materials were always analysed in the same analytical systems,
thereby complying with the PIT as much as possible. The only limitation of
the PIT is due to the chemical difference between unknown samples (CH4)
and the known reference materials (carbonates, H2O) used for anchoring
the CH4 gases to the respective isotope scales. In order to calibrate
the primary CH4 gases accurately, we need to exclude or eliminate
material- and method-specific errors (IAEA, 2003), which we discuss in the
following.
Quantitative oxidation of CH4 during δ13C–CH4
analysis requires high reaction temperatures (e.g. Dumke et al.,
1989). A major complication during δ13C–CH4
analysis arises when oxidation yields are significantly lower than 100 %
(Merritt et al., 1995; Fig. 4 in Sperlich et al., 2012). CH4
is a potent source of protonation in the IRMS ion source
(Anicich, 1993). Introducing unconverted CH4 together
with the CH4-derived CO2 sample into the IRMS results in the
formation of CO2H+ in the ion source, which produces an isobaric
interference on the m/z 45 trace, where the δ13C signal is
measured. This artefact can be prevented when CO2 and CH4 are
separated after the oxidation, which we achieve with the post-combustion
chromatographic column in both the EA–IRMS system (Sect. 2.3) and iSAAC
(Sect. 2.6). Note how this effect would cause an accuracy shift towards more
enriched δ13C–CH4 values predominantly during primary
CH4 gas calibrations, because CH4 samples would be affected by
CO2H+ formation in the ion source while the analysis of the used
CRMs would not.
We carefully checked the completeness of CH4 conversion (EA–IRMS and
TC/EA–IRMS) by monitoring for residual CH4 with the IRMS
instruments. In the ion source, CH4 molecules are subject to
fragmentation and re-combination processes, resulting in CH4-typical
mass spectra during mass abundance scans in the IRMS (Brunnée and
Voshage, 1964). The strongest CH4-specific signal occurs on the m/z 15
trace (CH3+), which makes the m/z 15 signal a good indicator for
incomplete CH4 conversion (Sperlich et al., 2012). The CH4+
signal at m/z 16 is not suitable for CH4 quantification due to the
interference with the O+ signal from CO2+ fragmentation. We tune
the m/z 44 collector of the IRMS to monitor the m/z 15 trace during the
analysis of a CH4 sample and find an amplitude of 0.12 mV. From
Sperlich et al. (2012) we estimate that about 40 % of the total CH4
signal in a mass abundance scan is recorded on m/z 15. The total CH4
signal in the mass abundance scan would therefore amount to ∼ 0.3 mV,
which we can compare to the ∼ 7000 mV on m/z 44 from a typical
CH4 injection into the EA–IRMS (e.g. Fig. 5). This approximation
suggests a CH4 oxidation efficiency of > 99.9 %. An
analogue experiment on the TC/EA–IRMS system (Sect. 2.2) shows a
conversion efficiency of CH4 of > 99.9 % as well.
Because the ionisation energy of CH4 is comparable to that of both
CO2 and H2, we can ignore this effect in the above determinations.
Therefore, we conclude that the CH4 conversion at MPI-BGC is complete
and that we can rule out incomplete conversion as source for measurement
errors.
It has been demonstrated that the introduction of carbonates into the
high-temperature oxidation furnace of the EA–IRMS yields a high CO2
conversion rate and δ13C results of high precision and accuracy
(Coplen et al., 2006b). In order to test for the completeness
of carbonate digestion, we added tungsten trioxide (WO3) to some of the
carbonate samples during weighing (about 1:1 by weight). The goal of this
experiment is to increase the instantaneous reaction temperature and to
provide additional oxygen during the liberation of CO2 from different
carbonates. While the addition of WO3 had no effect on the analysis of
CaCO3 and Li2CO3, it improved the peak shape during
BaCO3 analysis (Table 2). However, it did not impact on its δ13C. We conclude that the carbonate digestion is not limited by either
temperature or oxygen availability and omitted the addition of WO3 in
further reactions. Note that the accurate analysis of carbonates is critical
for accurate CH4 calibrations, even if CH4 injections themselves
are not compromised.
A considerable advantage of the conversion of carbonates in the
high-temperature oxidation furnace of the EA–IRMS over other methods (e.g.
acid reaction) is that the oxygen isotopic composition is homogenised for
all samples. This balances the 17O correction, which accounts for the
isobaric interference between δ13C–CO2 and δ17O–CO2 on m/z 45. The 17O correction is statistically
dependent on the δ18O–CO2 of each individual sample.
Hence, any uncertainty arising from the 17O correction during the
calculation of δ13C values from m/z 45 ion currents tends to
cancel out. The applied 17O correction is a function built into the
evaluation software of the IRMS. The algorithm and ratio assumptions are
based on Assonov and Brenninkmeijer (2001). The same technique
had been used to revise the VPDB scale by adding LSVEC as a second scaling
point (Coplen et al., 2006b).
Comparison of δ2H results between CIC and MPI-BGC.
Indices of the header are explained in Sect. 2.9 of the main text. The
uncertainty of all data includes the full traceability chain (Sect. 2.4),
which includes revised uncertainties of the CIC data (Sect. 2.9). The
difference Fossil - Biogenic allows us to compare scale compression
effects between both methods.
Gas name
δ2HCIC-old [‰]
δ2HMPI-BGC [‰]
Fossil
-170.1 ± 0.9
-171.9 ± 0.9
Biogenic
-317.4 ± 0.9
-319.8 ± 0.8
Fossil-Biogenic
147.3
147.9
The EA–IRMS analysis of carbonates includes a well-characterised blank
contribution that is due to the carbon impurities within the tin capsules
that are used for carbonate analyses (Werner et al., 1999). In
contrast, no such blank is expected when samples are analysed without tin
capsules, as would be the case for gaseous CH4 samples. While we did
not observe a significant δ13C difference when tin capsules
were added to CH4 injections and the δ13C bias was
subsequently corrected for or when the δ13C–CH4 analysis
was performed without tin capsules. We continuously added the tin capsules
to each δ13C–CH4 analysis and applied the routine blank
correction to all measurements in compliance with the PIT between analyses
of carbonate reference materials and CH4 samples.
For δ2H analyses, we chose an analogue approach and process
both H2O and CH4 using the high-temperature reactor of the
TC/EA–IRMS system. Possible artefacts can arise mainly from the
stronger surface activities of H2O vs. CH4 prior to the conversion
to H2 (and CO or carbon). H2O injections can lead to memory
effects, which need to be taken into account in δ2H–H2O and
subsequent δ2H–CH4 analyses, either by discarding initial
injections or making appropriate corrections (Werner and Brand, 2001).
H2O injections produced highest H2 yields and stable δ2H–H2O values at reactor temperatures of 1450 ∘C.
Therefore we kept the reactor at 1450 ∘C during all calibration
measurements. In addition, we found a minor dependence of δ2H–H2O on the septum temperature. We experimentally determined a
septum temperature of 130 ∘C at which the effect on δ2H–H2O was insignificant and kept the septum at 130 ∘C
during all calibrations. We describe the experiments on reactor temperature
and septum temperature in Appendix A in more detail. Note that it is
essential to exclude systematic, material-specific errors to make H2O
and CH4 reactions directly comparable for δ2H calibration.
Based on these experiments we conclude that the δ2H–CH4
calibrations do not contain measurement errors introduced by bracketing
δ2H–H2O analyses.
Differences in δ2H–CH4 and δ13C–CH4 between primary/secondary CH4 gas calibrations and
iSAAC measurements of the synthetic CH4-in-air standards using the scale
anchor based on Carina-1. Differences are calculated as δiSAAC–δpure. The bottom line shows the average
and the standard deviation (1σ) of considered differences, excluding
the value of Biogenic (∘) as described in main text.
Gas name
Δδ2H–CH4 [‰]
Δδ13C–CH4 [‰]
Megan
3.9
0.05
Merlin
5.6
-0.04
Minion
2.7
-0.05
Melly
4.3
0.13
Mike-1
5.7
-0.03
Martha-1
3.2
-0.06
Fossil
5.1
0.19
Biogenic
3.0
0.31 (∘)
Average
+4.2±1.2
+0.03±0.10
Discussion of the comparison between CIC and MPI-BGC
We compare the results of δ2H–CH4 and δ13C–CH4 calibrations achieved by the two independent methods from
CIC and MPI-BGC in Tables 4 and 5. Note that the verification of the
principle calibration method (MPI-BGC) by an independent method (CIC) is
required for the preparation of QCMs when CRMs are not available (IAEA,
2003). The comparison between CIC and MPI-BGC is to some degree
representative of the situation of the community analysing atmospheric
δ2H–CH4 and δ13C–CH4 without access to
international reference air but locally produced or propagated standard
gases.
Even though there is no significant difference between the intercomparison
results for δ13C–CH4, and the difference in δ2H–CH4 is rather small, there seems to be a systematic pattern
that the samples combusted at CIC are generally more enriched in both
δ2H and δ13C (Tables 4 and 5). The cause for this
offset is not yet fully understood but will be discussed in more detail.
The δ13C–CH4 calibrations presented in Table 4 were made
on three different IRMS systems with three different working standards. All
δ13C measurements were corrected for potential scale
compression effects, except from the MPI-BGC∗ analyses, which were made on
an IRMS system specifically tuned to render scale compression effects for
δ13C, as demonstrated by Ghosh et al. (2005). Because the
difference in δ13C between Fossil and Biogenic is
remarkably well resolved in all comparison measurements (Table 4), we
conclude that our δ13C comparison does not suffer from a
significant scale compression error. Rather, the difference in δ13C between the methods seems related to the method of CH4
conversion. In principle, incomplete CH4 combustion in the experiments
at CIC would create a δ13C pattern where the affected
experiments appeared more enriched in δ13C. This is because the
remaining CH4 fraction in the combustion-derived CO2 gas would be
introduced into the dual-inlet IRMS together with the CO2, and form
CO2H+ ions, which creates an artefact on m/z 45 (Sect. 4.1). However,
we carefully tested every sample for residual CH4 and are confident
that the CH4 combustions at CIC have been complete. Therefore, we
cannot resolve this difference further.
We also observe a small δ2H–CH4 offset between CIC and
MPI-BGC. The δ2H measurements at CIC were made using
combustion-derived H2O with two different methods (TC/EA–IRMS
and CRDS). Moreover, the measurement procedures at CIC included WSs covering
the full VSMOW/SLAP scale. In contast, the direct δ2H–CH4
analysis of the secondary CH4 gases at MPI-BGC was performed as a
one-point calibration against Megan or Merlin with a
δ2H–CH4
similar to that of Fossil (Table 3). Please note that δ2H scale
compression often arises during the analysis of H2O because it interacts with all sorts of
surfaces in the analytical system. However, CH4 gas behaves very much
like pure H2 in the high-temperature conversion system and a careful
H3+-factor determination often results in accurate isotopic
distances. If the control of scale compression at MPI-BGC was limited due to
the one-point calibration, we would expect the isotopic difference between
Biogenic and Fossil to be smaller in the results from MPI-BGC than CIC.
However, this is clearly not the case. The isotopic difference between
Biogenic and Fossil (δFossil–δBiogenic)
appears to be very similar in the calibrations of both laboratories with
147.3 ‰ at CIC and 147.9 ‰ at MPI-BGC, even showing a
slightly larger difference at MPI-BGC (Table 5). Therefore, we are confident
that the observed, small δ2H offset is not caused by scale
compression effects in one of the laboratories. Moreover, the excellent
agreement between the experimentally controlled scale compression at CIC and
the method at MPI-BGC proves that the analysis at MPI-BGC is free of
significant scale compression artefacts over the tested isotopic range of
∼ 150 ‰.
The comparisons show small differences in the calibration results, but we
found no evidence that either one of the two analytical methods is more
accurate. Note that the difference in both δ2H–CH4 and
δ13C–CH4 exceeds the compatibility goal of
1 and 0.02 ‰ by a factor of 2 to
10 respectively (WMO, 2014). We interpret the results of this
comparison to reflect calibration differences between laboratories that are
to be expected, when CRMs are not available. Finally, we conclude that our
new method is as capable to calibrate CH4 gases to the international
isotope scales and that it is as accurate as the method presented by
Sperlich et al. (2012). However, we think that our new methods are more
suitable for the task to produce and maintain a suite of calibration gases
for the following reasons.
The methods at MPI-BGC are more time efficient than the method of
Sperlich et al. (2012). While the new methods at MPI-BGC can be used to
calibrate an entire suite of CH4 gases within a relatively short time,
the method of Sperlich et al. (2012) is capable of processing only one
sample per day.
The new MPI-BGC methods are based on continuous-flow IRMS and follow the
PIT to the highest possible degree. In comparison, the method of Sperlich et
al. (2012) is based on the combustion of CH4 in an offline
reactor, which requires re-oxidation after every sample and partial
dismantling of the system to retrieve the sample for isotopic analysis.
Because the analytical system at CIC could theoretically be at a different
state for every sample (oxidation state, air leak rate) and because the
system at CIC does not allow us to compare two CH4 gases directly against
each other, the methods at MPI-BGC are superior in the ability to fulfil the
PIT. Even though the method at CIC proved to be very reproducible, we cannot
rule out that a variation in the oxidation state of the reactor or an
undetected air leakage into the system would affect the analysis of some
CH4 samples more than others. Because fulfilling the PIT is of
paramount importance for isotope ratio analysis (e.g. Werner and Brand,
2001; Schimmelmann et al., 2016), we believe the method at
MPI-BGC is less vulnerable to measurement errors in future calibrations.
Discussion on the compatibility between the scale anchors for δ2H–CH4 and δ13C–CH4 as propagated from IMAU to
MPI-BGC and JRAS-M16
We interpret the excellent agreement between the δ13C and CH4
calibrations in Carina-1 and Carina-2 from IMAU (Table 2) that
both gases are precisely referenced and suitable for scale propagation from
IMAU to MPI-BGC. The synthetic CH4-in-air standards were analysed on
iSAAC for δ2H–CH4 and δ13C–CH4 and their
isotope values were assigned using a WS that was calibrated against
Carina-1. We can then interpret the δ13C difference between
the iSAAC measurement and the calibrated synthetic CH4-in-air standards
of +0.03 ± 0.10 ‰ as an accurate estimate for
the calibration offset between the propagated scale anchor at MPI-BGC and
the newly developed JRAS-M16.
Unfortunately, the situation is currently less straight-forward for
δ2H–CH4. The two WSs Carina-1 and Carina-2 were
calibrated at IMAU with a difference in δ2H–CH4 of
2.8 ‰ that was insignificant at the time (Table 2).
Because Carina-1 and Carina-2 appear indistinguishable in δ2H–CH4 when compared to iSAAC with a measurement precision for
δ2H–CH4 of 1.0 ‰ (Sect. 2.6), we
cannot determine the laboratory offset with the same certainty as for
δ13C–CH4. If either Carina-1 or Carina-2 were
representative for the calibrations at IMAU, the δ2H–CH4 offset between the laboratories would amount to +4.2 ± 1.2 or +1.4 ± 1.2 ‰
respectively. A further comparison that includes new measurements on the
current system at IMAU is required to determine the offset δ2H–CH4 accurately. This offset can be resolved, for example,
when a set of synthetic CH4-in-air standards (JRAS-M16) is
analysed at IMAU in future.
Discussion on possible use of synthetic CH4-in-air standards in
future
We demonstrated the ability to test the compatibility between IMAU and
MPI-BGC by comparing scale anchors that were previously propagated from IMAU
to MPI-BGC to JRAS-M16 gases. Future developments include an
inter-laboratory comparison to test whether a dedicated set of our synthetic
CH4-in-air standards (JRAS-M16) could provide a community anchor to the
VPDB and VSMOW scales with documented accuracy. A further important test
would be to determine to what extent the use of centrally calibrated
standard gases could increase compatibility. A recent incidence provides a
good example for the vulnerability of δ13C–CH4
observations in the atmosphere without suitable m-RM.
LSVEC, the second CRM anchor to the VPDB scale, has recently been discovered
to be less reliable than anticipated. Until further notice, LSVEC is
suggested to be treated with an enhanced δ13C uncertainty of
0.15 ‰ (S. Assonov, personal communication, 2016). It is important to
appreciate that this uncertainty is fully added to the uncertainty of
δ13C–CH4 measurements, due to the similarity of LSVEC
(-46.6 ‰) and tropospheric CH4
(-47.5 ‰) in δ13C. That is, the new
uncertainty of LSVEC contributes the largest component in the full error
budget of δ13C–CH4 analysis. Note that the suggested
uncertainty of LSVEC is (i) on the order of the seasonal δ13C–CH4 cycle in the Southern Hemisphere and (ii) a multiple of
the analytical precision of laboratories monitoring δ13C–CH4. If measurements of δ13C–CH4 considered
the new uncertainty for LSVEC, the significance of signals such as the
seasonal variability in the Southern Hemisphere would be lost on the cost of
a better representation of accuracy. Including the uncertainty of LSVEC may
further impact on the compatibility between several laboratories and, for
example, suggest an artificially imposed spatial δ13C–CH4
gradient, based on calibration artefacts. We advocate the scientific gain
when accuracy and compatibility are differentiated (WMO, 2014). The
community benefits from a referencing method that enables a compatibility
level that is smaller than the atmospheric δ13C–CH4 signal
to resolve spatiotemporal δ13C–CH4 differences as primary
goal. We think that establishing JRAS-M16 as community scale anchor could be
a valuable step towards reaching this goal. As appropriate for any scale
anchor that is intended to be usable for the whole community over long
periods of time, the scale anchors will have to be re-calibrated frequently
in order to detect possible drifts or to improve and correct previous
assignments. The results of these efforts will be made available to the
public at regular intervals.
We propose the distribution of JRAS-M16, a set of synthetic CH4-in-air
standards in 5 L glass flasks. While two JRAS-M16 gases shall be used as
calibration standard, an optional third JRAS-M16 gas can be used as unknown
that is calibrated against the known JRAS-M16 gases as measurement control
standard. This experiment would simulate the case when all participating
laboratories measure the same sample directly against the same m-RM using
the method that is otherwise applied to every sample in the respective
laboratory and has the potential to determine the achievable compatibility.
A further possibility to share the JRAS-M16 scale anchor would be to send
cylinders with air-WSs to MPI-BGC for calibration. Because a dedicated
target of this work is to achieve best possible accuracy with JRAS-M16, we
provide the uncertainty of the full traceability chain. Once a new CRM has
been found in replacement of LSVEC, the δ13C–CH4 and the
traceability chain of JRAS-M16 will be revised accordingly. This will also
be made upon future CRM revisions or replacements.