Carbon monoxide (CO) and nitrous oxide (N2O) are two
key parameters in the observation of the atmosphere, relevant to air
quality and climate change, respectively. For CO, various analytical
techniques have been in use over the last few decades. In contrast, N2O
was mainly measured using gas chromatography (GC) with an electron capture
detector (ECD). In recent years, new spectroscopic methods have become
available which are suitable for both CO and N2O. These include
infrared (IR) spectroscopic techniques such as cavity ring-down
spectroscopy (CRDS), off-axis integrated cavity output spectroscopy
(OA-ICOS) and Fourier transform infrared spectroscopy (FTIR). Corresponding
instruments became recently commercially available and are increasingly used
at atmospheric monitoring stations. We analysed results obtained through
performance audits conducted within the framework of the Global Atmosphere
Watch (GAW) quality management system of the World Meteorology Organization
(WMO). These results reveal that current spectroscopic measurement
techniques have clear advantages with respect to data quality objectives
compared to more traditional methods for measuring CO and N2O. Further,
they allow for a smooth continuation of historic CO and N2O time series.
However, special care is required concerning potential water vapour
interference on the CO amount fraction reported by near-IR CRDS instruments.
This is reflected in the results of parallel measurement campaigns, which
clearly indicate that drying the sample air leads to an improved accuracy
of CO measurements with such near-IR CRDS instruments.
Introduction
The Global Atmosphere Watch (GAW) Programme of the World Meteorological
Organization (WMO) coordinates a network of atmospheric composition
observations comprising 31 global stations, more than 400 regional stations,
and around 100 contributing stations operated by contributing networks
(GAWSIS, 2018). These stations provide long-term observations of
atmospheric greenhouse gases (GHGs) and reactive gases such as carbon
dioxide (CO2), methane (CH4), nitrous oxide (N2O), and carbon
monoxide (CO), which are essential for understanding the GHG budget, both
regionally and globally. To make full use of these observations, the
uncertainty of these measurements must be reduced in order to obtain
consistent data series with traceability to common reference standards.
Within the GAW programme, Central Calibration Laboratories (CCLs) provide
reference standards that are linked to internationally accepted calibration
scales (Rhoderick et al., 2016, 2018). In addition,
World Calibration Centres (WCCs) evaluate GAW stations through independent
assessments by on-site system and performance audits (Buchmann et
al., 2009). The Laboratory for Air Pollution/Environmental Technology of
the Swiss Federal Laboratories for Materials Science and Technology (Empa)
has been operating the WCC for carbon monoxide (CO), methane (CH4), carbon
dioxide (CO2), and surface ozone (WCC-Empa) since 1996 as a Swiss
contribution to the GAW programme and has conducted over 90 system and
performance audits over the past 20 years. Furthermore, WCC-Empa
collaborates closely with the WCC for nitrous oxide (WCC-N2O) hosted by
the Karlsruhe Institute of Technology (KIT) Institute of Meteorology and
Climate Research - Atmospheric Environmental Research (IMK-IFU) to increase the number of N2O audits. In
order to address scientific needs for interpreting regional or global-scale
atmospheric observations, the GAW programme sets ambitious network
compatibility goals, which are continuously reviewed and, if necessary,
revised during biannual meetings of the WMO/GAW community (WMO,
2018). Network compatibility goals are set for amount fraction ranges
observed in the unpolluted troposphere, while extended network compatibility
goals reflect the less stringent requirements for urban and regional studies
with larger local fluxes. The network compatibility goals currently stand at
2 nmol mol-1 for CO and 0.1 nmol mol-1 for N2O, whilst the extended goals
are set to 5 nmol mol-1 for CO and 0.3 nmol mol-1 for N2O. These goals
represent the maximum bias that can generally be tolerated in measurements
of well-mixed background air used in global models to infer regional fluxes.
Some network compatibility goals may not be currently achievable within
current measurement and/or scale transfer uncertainties. However, they are
targeted for applications which require the smallest possible bias among
different datasets or data providers, such as for the detection of small
trends and gradients (WMO, 2018).
In situ measurements of tropospheric CO and N2O have been available
since the late 1960s (Weiss, 1981; Khalil and Rasmussen, 1983, 1988).
While early measurements were mainly analysis results based on flask
samples, quasi-continuous measurements have been available since the early
1980s (Brunke et al., 1990). Although continuous measurements of CO
and N2O began approximately at the same time and were often collocated,
challenges with respect to the measurement techniques for continuous
measurements were completely different. Carbon monoxide shows high temporal
and spatial variability, whilst the detection of very small changes is
needed for N2O observations. In the past, atmospheric CO and N2O
measurements at remote locations were almost exclusively made by gas
chromatographic (GC) techniques. GC with an electron capture detector
(GC/ECD) was by far the most abundant measurement technique for N2O,
whereas flame ionisation detection (GC/FID) in combination with a methaniser
and GC with a mercuric oxide reduction detector (GC/HgO) were the two most
commonly used techniques for CO measurements (Zellweger
et al., 2009).
Recent years brought the rapid development of a variety of alternative CO
measurement techniques, and a variety of methods are now in use at
atmospheric monitoring sites. Common methods include GC techniques (Gros
et al., 1999; Novelli, 1999; van der Laan et al., 2009), non-dispersive
infrared absorption (NDIR) (Parrish et al., 1994; Nedelec et al., 2003),
vacuum ultra-violet resonance fluorescence (VURF) (Gerbig et
al., 1999), Fourier transform infrared (FTIR) absorption (Griffith et
al., 2012; Hammer et al., 2013a), near-IR cavity ring-down spectroscopy
(NIR-CRDS) (Chen et al., 2013; Yver Kwok et al., 2015), and systems using
quantum cascade lasers (QCLs) in the mid-infrared such as mid-IR CRDS,
off-axis integrated cavity output spectroscopy (OA-ICOS) (Baer et al.,
2002; Provencal et al., 2005), and quantum cascade tuneable infrared laser
direct absorption spectroscopy (QC-TILDAS) (McManus et
al., 2015).
Alternatives to GC-ECD for N2O are not as abundant, but several methods
have been proposed in recent years. These include instruments deploying
optical techniques in the mid-IR, e.g. CRDS spectroscopy, FTIR, OA-ICOS,
QC-TILDAS, and difference-frequency-generation-based (DFG-based) systems. Lebegue
et al. (2016) published a comprehensive overview
of these techniques as well as their performance under controlled
conditions.
The recently developed optical techniques for CO and N2O have clear
advantages concerning sensitivity, repeatability, linearity, time response,
and temporal coverage, resulting in new measurement setups and calibration
strategies. However, only a few published studies comparing spectroscopic
techniques with GC systems exist for CO (Zellweger et al., 2009, 2012; Ventrillard et al., 2017) and N2O (Vardag et
al., 2014; Lebegue et al., 2016). Such comparisons of traditional and new
techniques are crucial for a smooth continuation of multi-decadal time
series when introducing new analytical techniques.
In this paper, we analyse data collected during CO and N2O performance
audits made by WCC-Empa and WCC-N2O from 2002 through 2017 from the
perspective of the used measurement techniques. We further present ambient-air CO comparisons made with a NIR-CRDS travelling instrument during
WCC-Empa audits and show limitations of the NIR-CRDS technique with respect
to water vapour interference. Assessment of atmospheric measurements through
parallel measurements with a travelling instrument is complementary to
performance audits with travelling standards and round-robin experiments and
is thus an essential, valuable quality control measure (Hammer et al.,
2013b; Zellweger et al., 2016).
Experimental methods
System and performance audits (hereafter only called audits) by WCCs are part
of the quality management framework of the GAW programme (WMO, 2017a).
Empa is the designated WCC for CO (since 1997), and since 2009 a
collaboration between WCC-Empa and the WCC for N2O has allowed WCC-Empa
to include N2O comparisons during station audits. The concept of
station audits has been described elsewhere (Klausen et al., 2003;
Buchmann et al., 2009; Zellweger et al., 2016). WCCs use two different
approaches to conduct performance audits: (i) comparisons of travelling
standards (TSs), i.e. high-pressure cylinders with known nominal values of CO
and N2O amount fractions, and (ii) parallel measurements using a
travelling instrument (TI). The TS method is widely applied, while the TI
concept is used less frequently and limited to CO, CO2, and CH4 by WCC-Empa.
Comparisons using travelling standards
The audit concept using TS supplies gases from high-pressure cylinders,
usually dry natural air or synthetic air, to the instruments of the audited
station. Usually, multiple analyses of a set of three or more TSs are made and
averaged for the final assignment of the TS value by the audited laboratory.
Calibrations of the TS against reference standards before and after the station
audit ensure traceability to the CCL, which is run by the National Oceanic
and Atmospheric Administration Earth System Research Laboratory
(NOAA/ESRL). The results are then analysed by a linear regression of the
values measured by the station vs. the reference values assigned by the WCC.
At WCC-Empa, N2O and CO amount fractions in the TS have been calibrated since
2010 by an Aerodyne quantum cascade laser spectrometer (QC-TILDAS-CS,
Aerodyne Research Inc., MA, USA). Before that, an AL5001 vacuum ultra-violet resonance
fluorescence analyser (AL5001, Aerolaser GmbH, Germany) was used for
CO calibrations. Both instruments are described in more detail in Zellweger
et al. (2012). Amount fractions are assigned to the TS
using a set of several reference standards purchased from the CCL.
The WCC-N2O uses a set of TSs traceable to a set of tertiary standards,
which are regularly recalibrated against secondary standards at the CCL. For
N2O, the calibration scales in use were the WMO-X2000 for audits before
2006 and WMO-X2006 and X2006A (Hall et al., 2007; NOAA,
2018c) afterwards. CO refers to the WMO-X2000, WMO-X2004, X2014, and X2014A
(NOAA, 2018a) calibration scales.
We analysed WCC-Empa performance audit results based on the TS method for
carbon monoxide (2005–2017) and nitrous oxide (2009–2017), as well as
results of N2O audits conducted by the WCC-N2O (2002–2013). Details on
analytical techniques, instruments, and calibration scales of these audits
are summarised in Table 1 for CO and Table 2 for N2O. Since the focus
of the paper is on instrument performance, only comparisons involving fully
functional instruments were considered. Furthermore, if data have been
reprocessed due to any known biases, e.g. in working standards, only the
results of the final comparison were considered, since they best represent
the performance of the measurement instruments at the time of the audit. CO
audits made by WCC-Empa before 2005 were not considered for the comparison
due to the following reasons. (i) Stations and WCC-Empa were often not
referring to the same CO calibration scale. WCC-Empa was using the WMO-X2000
carbon monoxide scale, while many GAW stations were still reporting on the
older WMO-X88 scale (Novelli et al., 2003) or other scales.
(ii) WCC-Empa at that time based its calibration of travelling standards
only on CO standards above 185 nmol mol-1; the WMO-X2000 calibration scale had
linearity issues, which have been corrected by the use of the WMO-X2004,
X2014, and X2014A calibration scales. WCC-Empa continued using the WMO-X2000
calibration scale until 2011 but used only standards with an amount fraction
larger than 185 nmol mol-1. At these amount fractions, the difference between
the WMO-X2000 and WMO-X2004 CO scales are very small and questionably
significant within their uncertainties. We therefore consider these two
scales as being identical for calibrations made at WCC-Empa. For CO, the
assessment has been made in the same standardised way as for carbon dioxide
(CO2) and methane (CH4) described in Zellweger et al. (2016), while a slightly different approach has
been chosen for N2O due to the fact that ambient-air amount fractions
increased significantly during the period of observation. The results
section gives further details on the methodology.
CO performance audits using travelling standards from 2005 to 2017. Please note that CSIRO in the table refers to the Commonwealth Scientific and Industrial Research Organisation scale.
∗ Chemicals Evaluation and Research Institute, Japan.
Ambient-air comparisons
Assessments based on TS comparisons, e.g. during station audits or round-robin experiments, have limitations. They only cover the analytical system
and exclude other aspects that might also be relevant, such as inlet or
drying systems. The low water content of the TS may, for example, lead to a
systematic bias, especially for analysers based on spectroscopic techniques
with implemented water vapour correction algorithms. The assessment during
on-site audits should therefore include parallel measurements with a TI
whenever feasible (WMO, 2011, 2012, 2014, 2016, 2018).
WCC-Empa implemented this additional approach for CO, CO2, and CH4
audits in 2011. Details of the setup and procedure as well as results for
CO2 and CH4 are published in Zellweger et al. (2016). Audits involving parallel measurements
for CO were conducted using a NIR-CRDS analyser (G2401, Picarro Inc., USA)
as a travelling instrument. The Picarro G2401 instrument has an internal
water vapour correction mechanism for CO and reports the dry-air amount fraction only.
However, these factory-based corrections are often not adequate (Chen et al., 2013). Due to the higher
analytical noise compared to CO2 and CH4 measurements, corrections
require a more comprehensive approach (Rella et al., 2013).
The internal water vapour correction of the TI was evaluated using the water
droplet method (Zellweger et al., 2012; Rella et al., 2013).
Approximately 0.8 mL of ultra-pure water is injected into a constant flow of
about 500 mL min-1 of a dry working standard and delivered to the
instrument using a bypass overflow. The CO amount fraction of the standards
used for the determination of the water vapour interference ranged from 57
to 741 nmol mol-1. No dependency of the water vapour interference on the CO
amount fraction was observed. For the WCC-Empa CO analyser, the water vapour
influence on the CO amount fraction, which is already corrected by the
internal water vapour compensation of the Picarro instrument, was then
fitted by a quadratic function. Due to the relatively large uncertainties of
individual experiments, we were not able to determine a reliable correction
function and, therefore, relied on the factory settings for our experiments.
Parallel measurements with the TI of the following GAW stations are shown in
this paper:
Puy de Dôme (PUY), France, is a global GAW station that is part of the
European Integrated Carbon Observation System (ICOS). A separate inlet
system leading to the same location as the air intake of the station
analyser was in place for the comparison with the TI. An additional pump at
a flow rate of approximately 2 L min-1 flushed this WCC-Empa inlet line. For
the last days of the comparison, the TI sampled from the station inlet using
the same cryogenic dryer as the station instrument. During this period, the
air was dried to a dew point of approximately -50∘C.
Anmyeon-do (AMY), South Korea, is a regional GAW station run and managed by the
Environmental Meteorology Research Division of the National Institute of
Meteorological Sciences (NIMS). Air was taken with both instruments from the
AMY air inlet system, and the air was dried to a dew point of approximately
-50∘C using a cryogenic trap.
Table 3 gives an overview of the comparisons, including duration and
instruments used. Detailed information about the stations is available from
the GAW Station Information System (GAWSIS, 2018).
N2O audits with travelling standards performed by the WCC-N2O (2002–2013) and WCC-Empa (2009–2017). Please note that SIO in the table refers to the Scripps Institution of Oceanography scales.
* The difference between the two FTIR comparisons is due to an offset of the
working standard used in 2016 and is not related to the performance of the
analyser.
Overview of ambient-air CO comparison campaigns.
LocationCoordinatesStartEndStation instrumentTravelling instrumentPUY45.7723∘ N,11 Apr 201622 Jun 2016Picarro G2401Picarro G24012.9658∘ E#CFKADS-2161#CFKADS2098Dry measurementsHumid and dry measurementsAMY36.5383∘ N,31 Jul 20175 Sep 2017LGR N2O/CO-30-EPPicarro G2401126.3300∘ E#15-0213#CFKADS2098Dry measurementsDry measurementsResultsAnalysis of travelling standard comparison
One of the objectives of this work was to evaluate the performance of
instruments for measuring CO and N2O at remote atmospheric-research
observatories. Of particular interest is the question of whether modern
spectroscopic techniques such as NIR-CRDS, TILDAS, OA-ICOS, or FTIR have a
significant advantage compared to traditional methods and whether
spectroscopic techniques improve the results of the performance audits
carried out by the WCCs for the corresponding compounds with respect to
precision and uncertainty. WCC-Empa made 60 comparisons during station
audits using travelling standards for CO (2005–2017) and 20 for
N2O (2009–2017). In addition, WCC-N2O conducted 16
comparisons during station audits (2002–2013). Tables 1 and 2 show details of
analytical techniques and instruments of these comparisons for CO and
N2O, respectively. The three letter codes (GAW ID) refers to the
different stations (GAWSIS, 2018). Results of audits at the central
calibration facility run by the Centre for Atmosphere Watch and Services
(CAWAS) and of the greenhouse gas analysis on board the Civil Aircraft for
the Regular Investigation of the atmosphere Based on an Instrument Container
(CARIBIC) are also included in the comparisons.
Each of the audits shown in Tables 1 and 2 involved the comparison of a set
of travelling standards and was then evaluated by a linear regression analysis
of the measured values by the stations vs. the WCC assigned amount
fractions, which are traceable to the CCL. To judge whether the combinations
of the resulting slope and intercept meet the WMO/GAW network compatibility
or extended network compatibility goals, a previously described method for
CO2 and CH4 (Zellweger et al., 2016)
was applied to CO and N2O. For CO, the bias of 165 nmol mol-1, which is
the centre of the amount fraction range of 30–300 nmol mol-1, representing the
unpolluted troposphere (WMO, 2018), was plotted against the slope of
the individual travelling standard comparisons. This amount fraction range
sufficiently covers the inter-hemispheric gradient, year-to-year
variability, seasonal cycles as well as observed trends for the period of
consideration at remote stations. For N2O, using a fixed amount
fraction range however might not be appropriate due to the significant
upward trend of the N2O mixing ratio in the atmosphere over the past
decades. The range currently representing the unpolluted troposphere has
been recently identified as 325–335 nmol mol-1 (WMO, 2018), which
corresponds well to the mean global atmospheric N2O amount fraction of
328.9±0.1 nmol mol-1 observed in 2016 (WMO, 2017b). A trend
analysis made by Blunden and Arndt (2017) showed an annual
increase of about 0.8 nmol mol-1 per year over the last decade, which is in
agreement with a fairly constant annual growth rate of 0.81 nmol mol-1 per
year from 1977 until today as determined by the National Oceanic and
Atmospheric Administration (NOAA, 2018b). Based on this, our
analysis of N2O audit results was made using a variable amount fraction
range covering 10 nmol mol-1 with the centre being representative for the
unpolluted troposphere for the year of the audit. Table 2 gives the
corresponding ranges used for the analysis. This method allows displaying
the result of each individual CO and N2O audit involving comparisons
with travelling standards as a single dot in a bias vs. slope plot, similar
to CO2 and CH4 results presented by Zellweger et al. (2016).
Evaluation of CO comparisons
Figure 1 shows the bias in the centre of the relevant amount fraction of the
unpolluted troposphere of 30–300 nmol mol-1 CO vs. the slope for the CO
audits listed in Table 1. Perfect agreement would result in bias–slope
pairs of (0 nmol mol-1/1). The allowed bias–slope combinations meeting the
network compatibility (green area) and extended network compatibility goals
(yellow area) of 2 and 5 nmol mol-1 (WMO, 2018),
respectively, are indicated. The distribution of the observed biases and
slopes gives further information about potential systematic offsets, which
could be present either at the WCC or at the stations. If results are not
systematically biased (e.g. by different calibration scales), a normal
distribution of the observed bias and slope pairs around 0 nmol mol-1 (bias)
and 1.0 (slope) is expected. This was the case for the slope with a mean
value of 0.994 and dispersion (1σ) of 0.068, which is not significantly
different from 1 (t test, p=0.47). However, the bias with a mean value
of -2.6 nmol mol-1 and dispersion (1σ) of 8.7 nmol mol-1 (1σ) was
significantly different from 0 (t test, p=0.02). A potential reason for this
could be an upward drift in standards, which is common for CO in air mixtures
at ambient amount fractions (Novelli et al., 2003; Gomez-Pelaez et al.,
2013). Drift rates are usually on the order of up to 1 nmol mol-1 per year.
To account for this, WCC-Empa frequently retrieves reference standards from
the CCL. This might not always be the case at measurement sites. There,
standards are often in use over long periods without recalibration or
the acquisition of new standards. The use of standards having increased amount
fractions due to the drift of instrument calibration will then result in an
underestimation of ambient CO, which potentially explains the observed mean bias.
(a) CO bias at 165 nmol mol-1 vs. the slope of the audit for individual travelling standard comparisons. Different colours indicate different measurement techniques of the station analysers. Filled symbols refer to a comparison with the same calibration scale at the station and the WCC, while open symbols indicate a scale difference. The error bars correspond to the uncertainty of the slope and the bias (1σ). The green and yellow areas correspond to the WMO/GAW network compatibility and extended network compatibility goals for the amount fraction range of 30–300 nmol mol-1. (b) Detail of the red dotted box in (a).
Figure 1 shows that reaching the network compatibility goals for CO is
extremely challenging. The variety of measurement techniques is quite large
and shows clear performance differences between methods. Newer spectroscopic
techniques such as the QCL-based TILDAS or OA-ICOS spectroscopy (QCL hereafter)
or CRDS generally show better performance compared to GC methods or NDIR.
Moreover, they also yield higher data coverage due to the truly continuous
observations in contrast to the semi-continuous GC measurements and the less
frequently required application of reference gases compared to NDIR
measurements. Higher data coverage further reduces the uncertainty caused by
incomplete sampling. Figure 2 summarises the percentage of comparisons that
met the network compatibility and extended network compatibility goals for
(a) all comparisons, (b) for GC/HgO and GC/FID systems only, (c) NDIR
instruments only, (d) VURF instruments only, and (e) for NIR-CRDS and QCL
instruments. FTIR is not shown separately, since only two comparisons of one
instrument were made. Out of the 60 comparisons, only 13 (21.7 %)
met the network compatibility goal and an additional 14 (23.3 %) met
the extended goal in the amount fraction range relevant to the troposphere.
Good performance over the entire relevant amount fraction range is required,
since atmospheric CO variability is large and pollution episodes, e.g. through long-range transport, are common even at remote locations.
Calibration strategies therefore should cover the entire range, which is
easier to implement for techniques with a linear response such as VURF,
NIR-CRDS, and QCL. The analysis of the performance audit results shows that
90 % of the NIR-CRDS and QCL comparisons were meeting the network
compatibility or extended network compatibility goal, while this was the
case for less than 40 % of the NDIR analysers or GC systems. From the
total of 10 TS–NIR-CRDS/QCL comparisons, five (50 %)
were within ±2 nmol mol-1, and an additional four (40 %) were within ±5 nmol mol-1. The corresponding numbers are significantly smaller for GC-based
methods (total of 18 comparisons) and NDIR (total of 23 comparisons), which
clearly indicates an advantage of the recent methods compared to more
traditional techniques.
Percentage of CO performance audit results that were in the range of 30–300 nmol mol-1 within the WMO/GAW network compatibility goals (green), the extended network compatibility goals (yellow), or outside the network compatibility goals (red area) for (a) all comparisons, (b) GC systems, (c) NDIR analysers, (d) VURF analysers, and (e) NIR-CRDS and QCL systems.
However, these results also depend on calibration and potential issues or
differences in the calibration scales. For example, an instrument with
perfect repeatability and reproducibility but an incorrect calibration, e.g. by a
bias in the calibration standard, can be outside the quality goals only
because of calibration issues. In this case, the uncertainty of the linear
regression of the travelling standard comparison is expected to be smaller
compared to instruments with poorer repeatability and reproducibility.
Therefore, the uncertainty of the linear regression analysis is another
measure of the instrument performance. Figure 3 shows a boxplot of the
standard uncertainty of the slopes of all CO performance audits grouped by
different analytical techniques. The results also confirm the better
performance of the QCL and NIR-CRDS instruments compared to GC techniques
and NDIR. Interestingly, the performance of NDIR analysers and GC/HgO
systems is similar, but this is likely due to different reasons. While the
repeatability of GC/HgO systems is generally superior compared to NDIR,
appropriate compensation of the non-linearity remains obviously difficult
compared to the normally linear but noisy NDIR analysers, resulting in a
similar performance of both techniques in the field for the amount fraction
range from 30 to 300 nmol mol-1.
Boxplot of slope uncertainties from the regression analysis for the CO performance audits for different analytical techniques including the number of comparisons (n). The horizontal blue line denotes the median, and the blue boxes show the inter-quartile range.
(a)N2O bias in the centre of the relevant ambient-air amount fraction (Table 2) vs. the slope of the audit for individual travelling standard comparisons. Different colours indicate different measurement techniques of the station analysers. Filled symbols refer to a comparison with the same calibration scale at the station and the WCC, while open symbols indicate a scale difference. The error bars correspond to the uncertainty of the slope and the bias (1σ). The green and yellow areas correspond to the WMO/GAW network compatibility and extended network compatibility goals for the range of ±5 nmol mol-1 around the centre of the relevant amount fraction range, and the dashed green and yellow lines show the limits at the relevant amount fraction. (b) Detail of the red dotted box in (a).
Comparison with the recent WMO/IAEA Round Robin Comparison Experiment,
as done for N2O (see below), is not straightforward. Changes in the
calibration scale during the round-robin experiment jeopardises the direct
comparison of the audit results with the round-robin results.
Evaluation of <inline-formula><mml:math id="M240" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">N</mml:mi><mml:mn mathvariant="normal">2</mml:mn></mml:msub><mml:mi mathvariant="normal">O</mml:mi></mml:mrow></mml:math></inline-formula> comparisons
Figure 4 shows the bias in the centre of the relevant amount fraction range
of the year of the comparison vs. the slope for the N2O audits shown in
Table 2 along with the allowed bias–slope combinations meeting the network
compatibility (green area) and extended network compatibility goals (yellow
area) of 0.1 and 0.3 nmol mol-1 (WMO, 2018), respectively.
Only results of comparisons with fully functional instruments were
considered.
The results presented in Fig. 4 show that reaching the WMO/GAW network
compatibility goals remains difficult for N2O. However, calibration
ranges at stations can be intentionally limited to the ambient amount
fraction typical for their location and time. These ranges are normally
significantly smaller than those used in Fig. 4 in the case of N2O.
Therefore, bias–slope pairs outside the network compatibility goals do not
necessarily imply that the measurements at a station are biased, but they
are indicative of the performance of the instrument and its calibration over
a given amount fraction range. The dashed green and yellow lines in Fig. 4
denote the limits for meeting the network compatibility and extended network
compatibility goals at the relevant amount fraction.
(a) Percentage of N2O performance audit results that were for the range of the relevant amount fraction ±5 nmol mol-1 within the WMO/GAW network compatibility goals (green), the extended network compatibility goals (yellow), or outside the network compatibility goals (red). (b) Same as (a) but at the relevant amount fraction (see text for details).
(a) Percentage of the results of the sixth round-robin experiment that were for the range of the relevant amount fraction ±5 nmol mol-1 within the WMO/GAW network compatibility goals (green), the extended network compatibility goals (yellow), or outside the network compatibility goals (red area). (b) Same as (a) but at the relevant amount fraction (see text for details).
As discussed above for CO, the distribution of the observed biases and
slopes is an indicator of potential systematic offsets, either at the WCCs
or at the stations. No significant deviations were observed for audits
carried out by WCC-Empa, with a mean value of the bias of 0.32 nmol mol-1 and
a dispersion (1σ) of 1.09 nmol mol-1 (t test, p=0.11), and a mean
value of the slope of 0.965 with a dispersion (1σ) of 0.093 (t test, p=0.21). WCC-N2O comparisons showed no significant deviations with a
mean bias of -0.12 nmol mol-1 and a dispersion (1σ) of 0.89 nmol mol-1
(t test, p=0.35); however, the deviation of the slope with a mean value
of 0.954 and a dispersion (1σ) of 0.067 was significant (t test, p=0.01). This result indicates that at the launch of the audits in 2002 the
linearity problem of the ECD was not fully considered in the data evaluation
by the audited stations. The GC/ECD technique, which contributes most to the
results, is known to be highly non-linear (Lebegue et al., 2016), and consequently,
deviations are expected for amount fractions away from the relevant level if
the non-linearity of the systems had not been determined accurately enough.
With ongoing data quality assurance activities and the implementation of
linearity corrections for the ECD response, the slope now is close to 1 for
more recent performance audits.
Figure 5 presents the result of the above analysis as percentages of
comparisons meeting the network compatibility and extended network
compatibility goals. Until now, none of the performance audits conducted by
either the WCC-N2O or WCC-Empa achieved the compatibility goal of 0.1 nmol mol-1, and only one third of the results were within the extended goals
of 0.3 nmol mol-1 when an amount fraction range of 10 nmol mol-1 is considered.
This slightly improves if we consider only the bias at the relevant amount
fraction. The relevant amount fraction corresponds to the value at the
centre of the relevant range for the corresponding year. Under these less
stringent conditions, we find 19.4 % compliance with the network
compatibility goal and 36.1 % with the extended network compatibility
goal. This is in line with the small variations in N2O
at remote locations and the corresponding limited calibration range of many
stations mentioned above. Lebegue et al. (2016) recognised that
measurements of small variations in the N2O amount fractions using
GC/ECD is very challenging, which is in agreement with the TS comparison
results from the station audits of this work.
The results obtained during the performance audits by WCC-Empa and the
WCC-N2O compare well with the recent WMO/IAEA Round Robin Comparison
Experiment organised and coordinated by the CCL for N2O hosted by NOAA.
The sixth round-robin experiment took place in 2014–2015, and involved the
comparison of two standards, one containing a lower (average of 321.6 nmol mol-1)
and the other a higher (average of 333.7 nmol mol-1) N2O amount fraction
(NOAA, 2018d). A total of 25 laboratories participated in this
exercise. With this dataset, we made the same analysis as described above
after the exclusion of two laboratories using calibration scales other than
WMO-X2006A. The percentage of laboratories fulfilling the WMO network
compatibility and extended network compatibility goal was very similar to
the results from the station audits by WCC-Empa and the WCC-N2O, as shown
in Fig. 6.
Comparison of hourly averages of CO at PUY between the WCC-Empa travelling instrument and the PUY Picarro G2401 for the period when the TI sampled humid air. (a) CO time series. (b) CO bias of the station analyser vs. time. The green and yellow areas correspond to the WMO network compatibility and extended network compatibility goals.
Bias of the PUY Picarro G2401 vs. the water vapour measured by the TI. The solid black line shows the linear regression with a 95 % confidence interval (dashed lines). The green and yellow areas correspond to the WMO network compatibility and extended network compatibility goals.
CO (humid, corrected) / CO (dry) vs. the reported water vapour for the experiment before (a) (23 March 2016) and after (b) (14 July 2016) the comparison at PUY.
Out of the 25 laboratories in the round-robin experiment, only two (8 %)
were entirely within the WMO/GAW network compatibility goal of 0.1 nmol mol-1
for the 10 nmol mol-1 range. At the relevant amount fraction, the percentage
of laboratories that were not meeting the quality goals was very similar for
the WCC audits (44 %) and the round-robin experiment (40 %).
The above results, both for TS comparisons during audits and the round-robin
experiment, are clearly illustrating that it remains highly challenging to
reach the network compatibility and extended network compatibility goals for
N2O. In contrast to advances made for the detection of CH4,
CO2 (Zellweger et al., 2016), and CO,
measurements of N2O were in most cases still based on gas
chromatography, and only a few recent comparisons involved spectroscopic
techniques. The data for N2O clearly indicate the advantages of the
spectroscopic techniques compared to gas chromatography. The uncertainty of
the observed intercepts and slopes of the linear regression gives
information on the linearity and repeatability of the system. The
uncertainty of the slope of the linear regression was significantly smaller
for QCL and FTIR analysers (median of 0.0028, standard deviation of 0.0031)
compared to GC/ECD systems (median of 0.0126, standard deviation of 0.0284).
Despite the better performance regarding the linearity and repeatability of the
spectroscopic techniques compared to GC/ECD, no clear advantage of the
spectroscopic methods was observed during the performance audits. A
potential reason could be the uncertainty of the calibration standards,
which in the case of N2O is of the same order or even larger than the
WMO/GAW network compatibility goal. The CCL determined a reproducibility of
N2O calibrations in the ambient range of ∼0.22 nmol mol-1
(95 % confidence level) (Hall et al., 2007; NOAA,
2018c), which is larger than the network compatibility goal. However, this
uncertainty is low compared to uncertainties associated with the gravimetric
preparation of standards, which highlights the importance of maintaining and
propagating calibration scales (Brewer et al., 2018) as
implemented in the WMO/GAW programme. Therefore, it is yet too early to
quantify this improved performance of spectroscopic techniques for N2O
and give a final statement with respect to the network compatibility goals.
Ratios of CO (humid, corrected) / CO (dry) amount fractions vs. the water vapour mixing ratios of two different Picarro G2401 NIR-CRDS analysers over time. The legend shows the date (dd-mm-yy) of the experiment. The coloured areas are the limits for the WMO/GAW network compatibility goal (green) and extended (yellow) network compatibility goal at the amount fraction of 300 nmol mol-1 CO.
Comparison of hourly averages of CO at PUY between the WCC-Empa travelling instrument and the PUY Picarro G2401 for the period when the TI sampled dry air. (a) CO time series. (b) CO bias of the station analyser vs. time. The green and yellow areas correspond to the WMO network compatibility and extended network compatibility goals.
Bias of the PUY Picarro G2401 CO instrument vs. WCC-Empa assigned values. Black dots represent the average of the data at a given level from a specific TS comparison. The error bars show the standard deviation of individual measurement points. The green and yellow lines correspond to the WMO network compatibility and extended network compatibility goals, and the green and yellow areas to the amount fraction range relevant for PUY. The dashed lines around the regression lines are the Working–Hotelling 95 percentage confidence intervals. The coloured dots show the bias during the ambient-air comparison without (blue) and with (red) drying of the air sampled by the TI.
Comparison of hourly averages of CO at AMY between the WCC-Empa travelling instrument and the AMY Los Gatos 30-EP QCL analyser. Both instruments sampled dry ambient air. (a) CO time series. (b) CO bias of the station analyser vs. time. The green and yellow areas correspond to the WMO network compatibility and extended network compatibility goals. The dashed vertical lines indicate the time of the calibration of the AMY instrument.
Bias of the AMY Los Gatos 30-EP CO instrument vs. WCC-Empa assigned values. Black dots represent the average of the data at a given level from a specific TS comparison. The error bars show the standard deviation of individual measurement points. The green and yellow lines correspond to the WMO network compatibility and extended network compatibility goals, and the green and yellow areas to the amount fraction range relevant for PUY. The dashed lines around the regression lines are the Working–Hotelling 95 % confidence intervals. The red dots show the bias during the ambient-air comparison.
Ambient-air comparisons
The above results, as well as round-robin experiments, are travelling
standard comparisons and are therefore not covering all aspects of ambient-air measurements. Other aspects include bias due to sampling procedures,
drying, or potentially relevant insufficient accounting of spectral
interferences, e.g. by water vapour. For example, Chen et al. (2013) demonstrated that accurate measurements
of CO in humid air is possible with the NIR-CRDS technique implemented by
Picarro. Correction functions however are different for each individual
instrument, and as a result of the work of Chen et al. (2013), these functions have been implemented in
Picarro NIR-CRDS CO analysers since 2012.
WCC-Empa started with parallel measurements of ambient air for CO, CO2,
and CH4 during station audits in 2011. The results of the greenhouse
gas comparisons showed that additional information, e.g. related to air
inlet systems, is obtained by these comparisons (Zellweger et al., 2016). However, these
comparisons were in many cases less conclusive for CO. Some parallel
measurements showed differences that were not present in the travelling
standard comparisons. Sampling issues were unlikely because the ambient-air
comparison of CH4 and CO2 agreed well. Therefore, other issues
like interferences of ambient-air constituents may cause an additional bias.
For example, the comparison made at the global GAW station Puy de Dôme
(PUY) in 2016 showed significant deviations in ambient CO measurements, as
illustrated in Fig. 7, while the TS comparison showed good agreement.
During this period, the PUY analyser was measuring on average 5.85 nmol mol-1
(standard deviation of 0.94 nmol mol-1) higher than the TI. Despite this bias,
both instruments captured the temporal variation well. The WCC-Empa
travelling instrument was sampling from the same air intake location but
with a completely independent sampling line. In contrast to the PUY
instrument, which sampled air dried to a dew point of -50∘C, the
air sampled by the travelling instrument was not dried. As discussed in
Sect. 2.2, the factory-implemented water vapour correction was used. The
observed bias correlates with the measured water vapour, as shown in Fig. 8, which indicates issues with the internal water vapour compensation of the
TI. Water vapour correction functions of this instrument were determined
three weeks before and three weeks after the comparison campaign with a
droplet test, in analogy to the method described by Rella et al. (2013). Figure 9 shows the ratio of
CO (humid, corrected) / CO (dry) against the measured water vapour content of
the TI; CO (dry) is the amount fraction measured by the instrument in the
absence of water, and CO (humid, corrected) the water-vapour-corrected CO
amount fraction reported by the Picarro G2401 during the humidification by
the droplet test. Since the Picarro G2401 reports CO only as a dry-air amount
fraction, the measured ratio should be equal to 1 and not depend on water
vapour content. However, a significant change in the CO response in relation
to water vapour was observed. The TI was underestimating the CO amount
fraction in the experiment before the campaign (Fig. 9a), and it then changed
to an overestimation after the campaign (Fig. 9b). Possibly, this has been
influenced by the upgrade to a new software version of the TI between the
two periods. Unlike for CO2 and CH4, individual water vapour
correction functions for CO can currently not be determined with sufficient
accuracy to achieve the WMO/GAW network compatibility goal of 2 nmol mol-1.
Individual experiments using the droplet test have a large uncertainty due
to higher instrumental noise for CO compared to CH4 or CO2.
Furthermore, CO correction functions seem to be less stable over time, and
sudden changes are possible. Figure 10 shows fitted ratios of CO (humid, corrected) / CO (dry) vs. the measured water vapour content for two different
instruments over a period of several years. Both instruments show
significant variation over time in the humidity-corrected CO reported by the
analyser. Consequently, drying of the sample air could improve CO
measurements with Picarro G2401 instruments and likely with Picarro G1302
and G2302 CO/CO2/H2O analysers. This has been confirmed by a
period of dry ambient-air measurements of both instruments at PUY. Figure 11
shows the comparison of the two analysers during the audit collocation
measurement. In this case, the TI was connected to the same sampling line as
the PUY instrument after the cryogenic trap, and both instruments were
measuring dry air. The bias of the PUY analyser significantly decreased to
1.20 nmol mol-1 with a dispersion (1σ) of 0.57 nmol mol-1. This agrees well
with the observed bias during the travelling standard comparison.
Potentially, the change of the inlet system could also have been the reason
for the reduction in the bias. However, this is unlikely because no change
in the bias of CH4 and CO2 amount fraction, which were both
measured simultaneously together with CO over the same inlet line, was
observed. Figure 12 summarises the results of the performance audits at
PUY with TS, as well as the bias observed during the comparison campaign
with humid and dry measurement of the TI.
Figure 13 shows another example of a CO ambient-air comparison made at the
regional GAW station Anmyeon-do, South Korea, over a period of 1 month in
2017. The comparison was made between the AMY OA-ICOS analyser (LGR-30-EP,
Los Gatos Research, USA) and the WCC-Empa Picarro G2401 travelling
instrument. Both analysers were measuring ambient air dried to a dew point
of -50∘C using a cryogenic trap. Temporal variability at this
site is significantly larger compared to PUY, and except for a few spikes,
it was well captured by both instruments. The bias of the AMY analyser
averaged to 0.10 nmol mol-1 with a dispersion (1σ) of 3.20 nmol mol-1 over
the entire period of the campaign. However, during the first third of the
campaign, the AMY instrument was slightly underestimating the CO amount
fraction compared to WCC-Empa (bias of -2.28 nmol mol-1, dispersion of 2.91 nmol mol-1), followed by a slight overestimation in the second third (bias of 1.47 nmol mol-1, dispersion of 2.81 nmol mol-1). The last third then showed good
agreement between the two systems (bias of 0.57 nmol mol-1, dispersion of 2.80 nmol mol-1). These differences are likely due to different calibration
strategies. The TI was measuring three standard gases to calibrate and
compensate for drift of the instrument every 30 h. In contrast, manual
calibrations were made of the AMY analyser every 14 d with one
calibration standard (dried ambient air traceable to the WMO-X2014A scale),
applied as a stepwise change fortnightly, and with no further corrections
applied in the meantime. These manual calibrations coincide with the
observed change in the bias. Consequently, more frequent calibrations or
automated measurements of a working standard to compensate for drift would
have further improved the agreement. The ambient-air measurements made at
AMY were also in agreement with the TS comparison, which is illustrated in
Fig. 14. The scatter in the bias is significantly larger for ambient-air
measurements compared to the TS comparison. Firstly, part of this may be
explained by the calibration strategy, as discussed above. Secondly,
differences in the response time for both instrument types as well as
residence time in the inlet might further add to the observed scatter,
especially in case of rapid changes in the CO amount fraction, which
frequently occurred at AMY.
Both campaigns show that accurate measurements of CO are possible if the
sample air is dried. So far, this has not yet been implemented at all
measurement stations. The above case study at PUY as well as the experiments
done involving the droplet tests only investigated the internally
implemented water vapour correction of the Picarro G2401, which proved to be
not sufficiently stable enough to achieve the network compatibility goals of the
WMO/GAW programme. Alternatively, better determination of the remaining
water vapour interference is needed. The droplet method might not be
suitable due to the relatively fast drying process, which results in
relatively high uncertainties due to the analyser's noise. Alternative
methods, e.g. as described by Reum et al. (2019) or as
implemented by the ICOS Metrology Laboratory, which uses a Bronkhorst vapour
delivery module (VDM) to humidify a gas stream from a tank, might give
better results. In addition to improvements of the droplet method,
alternative ways to compensate for the water-vapour-dependent CO bias need
to be explored. Chen et al. (2013) showed that
the main uncertainty of the water vapour correction is due to the fact that
the weak CO absorption line is bracketed by adjacent absorption lines of
CO2 and H2O. Our results indicate that the compensation of the
water vapour interference based on the work of Chen et al. (2013), which has been implemented in Picarro
analysers since 2012, does not appropriately correct all the bias and
may change over time. Therefore, frequent determination of the water vapour
interference will be needed to ensure long-term stability of the correction
function or to characterise its change over time. However, this will most
likely be insufficient to detect the sudden changes in the correction
function that were observed in our experiments. Consequently, drying the
sample air should be considered when measuring CO with a Picarro G2401
instrument. Both cryogenic traps and Nafion dryers can be used. WCC-Empa now
uses Nafion dryers for the parallel measurements during station audits. Both
single tube (MD-070-48S-4) and multi-tube (PD-50T-12MPS) Nafion dryers in
reflux mode using the Picarro pump for the vacuum in the purge air were
employed successfully. This reduced the amount of water to approximately
0.06 %–0.22 % (single tube) and 0.01 %–0.03 % (multi-tube), depending on
ambient-air humidity. In case of using Nafion dryers, the standard gases
must be passed though the dryer to compensate for a potential loss in the dryer.
Conclusions
The different elements of the WMO/GAW quality management framework,
including round-robin experiments, performance audits with travelling
standards, and parallel measurements at stations provide complementary
information, which is essential for reducing the bias and uncertainty of time
series measured by atmospheric-research stations.
The assessment of performance audit results of CO and N2O with respect
to different measurement techniques showed clear advantages of newer
spectroscopic techniques such as NIR-CRDS or QCL spectroscopy in the case of
CO. However, parallel measurements made using a Picarro NIR-CRDS analyser
identified issues with the implemented water vapour compensation, and
further improvement is currently only possible by drying the sample air.
This can be implemented though drying the sample air with either
cryogenic traps or Nafion dryers.
For N2O, one of the limitations is the uncertainty of calibration
standards. This highlights the importance of maintaining traceability to an
internationally accepted calibration scale as implemented by the GAW
programme.
By introducing modern spectroscopic measurement techniques such as CRDS or
QCL, the number of GAW stations complying with the WMO/GAW network
compatibility goals for CO and N2O will increase. However, reaching the
network compatibility goal of 2 nmol mol-1 for CO and 0.1 nmol mol-1 for
N2O will remain challenging. Careful calibration strategies and
appropriate water vapour corrections or drying the sample air are
required for both CO and N2O.
Data availability
Data from the performance audits made by
WCC-Empa are available from the corresponding audit reports (http://www.empa.ch/web/s503/wcc-empa, last access: 30 October 2019). Data of the WMO/IAEA Round Robin
Comparison Experiment are publicly available on the NOAA Earth System
Research Laboratory Global Monitoring Division web page (https://www.esrl.noaa.gov/gmd/ccgg/wmorr, last access: 30 October 2019). Other data used in the paper are
available upon request to the corresponding author.
Author contributions
CZ led and designed this study. BB supervises the activities of WCC-Empa. RS
made N2O comparisons during station audits of the WCC-N2O and
provided N2O comparison data. CZ and MS performed CO and N2O
comparisons during station audits by WCC-Empa. OL contributed to in situ
measurements of CO at Puy de Dôme. HL and SK contributed to in situ
measurements of CO at Anmyeon-do. CZ wrote the paper. All co-authors
(RS, OL, HL, SK, LE, MS, BB) were involved in scientific discussions and
commenting on the paper.
Competing interests
The authors declare that they have no conflict of interest.
Special issue statement
This article is part of the special issue “The 10th International Carbon Dioxide Conference (ICDC10) and the 19th WMO/IAEA Meeting on Carbon Dioxide, other Greenhouse Gases and Related Measurement Techniques (GGMT-2017) (AMT/ACP/BG/CP/ESD inter-journal SI)”. It is a result of the 19th WMO/IAEA Meeting on Carbon Dioxide, Other Greenhouse Gases, and Related Measurement Techniques (GGMT-2017), Empa Dübendorf, Switzerland, 27–31 August 2017.
Acknowledgements
MeteoSwiss supported this work through its engagement
in the Global Atmosphere Watch programme. We further acknowledge the support
by the station staff at various GAW stations during the audits. Haeyoung Lee
and Sumin Kim would like to acknowledge support from the Korea
Meteorological Administration Research and Development Program “Research and Development for KMA Weather, Climate, and Earth System Services – Development of Monitoring and Analysis Techniques for Atmospheric Composition in Korea” (grant no. 1365003041). Eckhart Scheel, who sadly passed
away in 2013, mainly conducted WCC-N2O audits. Further, the WCC-N2O
acknowledges the support of the German Environment Agency.
Review statement
This paper was edited by Christof Janssen and reviewed by three anonymous referees.
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