AMTAtmospheric Measurement TechniquesAMTAtmos. Meas. Tech.1867-8548Copernicus PublicationsGöttingen, Germany10.5194/amt-12-1013-2019Correcting atmospheric CO2 and CH4 mole fractions obtained with
Picarro analyzers for sensitivity of cavity pressure to water vaporCorrecting atmospheric CO2 and CH4 mole fractionsReumFriedemannfreum@bgc-jena.mpg.dehttps://orcid.org/0000-0003-2488-6582GerbigChristophhttps://orcid.org/0000-0002-1112-8603LavricJost V.https://orcid.org/0000-0003-3610-9078RellaChris W.GöckedeMathiashttps://orcid.org/0000-0003-2833-8401Max Planck Institute for Biogeochemistry, Jena, GermanyPicarro Inc., Santa Clara, CA, USAFriedemann Reum (freum@bgc-jena.mpg.de)15February20191221013102720July201821August201811January201922January2019This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/This article is available from https://amt.copernicus.org/articles/12/1013/2019/amt-12-1013-2019.htmlThe full text article is available as a PDF file from https://amt.copernicus.org/articles/12/1013/2019/amt-12-1013-2019.pdf
Measurements of dry air mole fractions of atmospheric
greenhouse gases are used in inverse models of atmospheric tracer transport
to quantify their sources and sinks. The measurements have to be calibrated
to a common scale to avoid bias in the inferred fluxes. For this purpose,
the World Meteorological Organization (WMO) has set requirements for the
interlaboratory compatibility of atmospheric greenhouse gas (GHG)
measurements. A widely used series of devices for these measurements are the
GHG analyzers manufactured by Picarro, Inc. These are often operated in
humid air, and the effects of water vapor are corrected for in
post-processing. Here, we report on rarely detected and previously
unexplained biases of the water correction method for CO2 and CH4
in the literature. They are largest at water vapor mole fractions below 0.5 % H2O, which were undersampled in previous studies, and can
therefore affect measurements obtained in humid air. Setups that dry sample
air using Nafion membranes may be affected as well if there are differences
in residual water vapor levels between sample and calibration air. The
biases are caused by a sensitivity of the pressure in the measurement cavity
to water vapor. We correct these biases by modifying the water correction
method from the literature. Our method relies on experiments that maintain
stable water vapor levels to allow equilibration of cavity pressure. In our
experiments with the commonly used droplet method, this requirement was not
fulfilled. Correcting CO2 measurements proved challenging, presumably
because of our humidification method. Open questions pertain to differences
among analyzers and variability over time. In our experiments, the biases
amounted to considerable fractions of the WMO interlaboratory compatibility
goals. Since measurements of dry air mole fractions of CO2 and CH4
are also subject to other uncertainties, correcting the cavity
pressure-related biases helps keep the overall accuracy of measurements
obtained with Picarro GHG analyzers in humid and potentially in Nafion-dried
air within the WMO goals.
Introduction
Measurements of atmospheric greenhouse gas (GHG) mole fractions are integral data for
quantifying their sources and sinks using inverse models of atmospheric
transport (e.g.,
Kirschke et al., 2013; McGuire et al., 2012). Inverse models require
atmospheric measurements calibrated to a common scale because relative
biases in the atmospheric mole fractions lead to biases in the inferred
fluxes. To ensure the high quality of GHG observations required
for inverse models of atmospheric transport, the World Meteorological
Organization (WMO) has set compatibility goals for atmospheric CO2 and
CH4 measurements to ±0.1 ppm for CO2 (±0.05 ppm in
the Southern Hemisphere) and ±2 ppb for CH4 (WMO,
2016) among laboratories. This compatibility is ensured if individual
laboratories keep errors of measurements with respect to a common
calibration scale below half of these goals, which corresponds to the
so-called internal reproducibility goals (WMO, 2016). Models of
atmospheric GHG transport require dry air mole fractions as
input, i.e., the number of molecules of the target gas divided by the number
of air molecules excluding water vapor. Water vapor is excluded because its
variability would mask signals in the GHGs.
GHG analyzers manufactured by Picarro Inc. (Santa Clara, CA), which are
based on the cavity ring-down spectroscopy technique
(Crosson, 2008), are used at many GHG
monitoring sites because of their signal stability. Due to limitations of
air sample drying techniques
(Rella et al.,
2013), these analyzers are often operated in humid air, and dry air mole
fractions are obtained by correcting for the effects of water vapor in a
post-processing step
(Chen
et al., 2010; Rella et al., 2013). The effect of water vapor on trace gas
readings can be described by a water correction function fch, where c denotes the target gas (here CO2 or CH4) and h
is the water vapor mole fraction (measured by the Picarro analyzer). The
analyzer reports wet air mole fractions cweth, from
which dry air mole fractions cdry can be obtained by dividing by the
water correction function:
cdry=cwethfch.
The water correction function from the literature takes into account
dilution and line shape effects. These are described by a second-degree
Taylor series, i.e., a parabola
(Chen
et al., 2010; Rella et al., 2013):
fcparah=1+ac⋅h+bc⋅h2.
Thus, dry air mole fractions based on this model are calculated as
cdrystandard=cwethfcparah.
Henceforth, we call this the “standard” water correction model.
In previous studies featuring water corrections for CO2 and CH4,
water vapor mole fractions below 0.5 % H2O were only scarcely
sampled
(Chen
et al., 2010; Nara et al., 2012; Rella et al., 2013; Winderlich et al.,
2010). In this paper, we report on biases in cdrystandard in this
domain that were not detected in these previous studies. They were, however,
recently detected in one other study in which this domain was sufficiently
sampled
(Stavert
et al., 2018). We hypothesize that the biases in CO2 and CH4
readings are due to an as-yet-undocumented sensitivity of the pressure
inside the measurement cavity to water vapor. We designed and conducted
experiments that uncovered that the internal pressure sensor, which is used
to stabilize cavity pressure, produces erroneous readings in the presence of
water vapor. These errors cause a sensitivity of cavity pressure to water
vapor that translates into biases in CO2 and CH4 readings. Thus,
the hypothesis was confirmed. Based on these results, we provide an approach
to correct the biases in CO2 and CH4 readings. We also discuss
remaining challenges, which are related to the reliable correction of
CO2 readings as well as differences among analyzers and variability
over time.
Materials and methods
To determine the effect of water vapor on CO2 and CH4 measurements
obtained using Picarro analyzers, as well as on the pressure in the
measurement cavity, so-called “water correction” experiments similar to
those in the literature
(e.g.,
Rella et al., 2013) were performed; i.e., dry air from pressurized gas tanks
was humidified and measured with Picarro GHG analyzers. Dry air mole
fractions used were in the ranges of 352–426 ppm CO2 and 1797–2115 ppb
CH4. The key modifications to the experiments in the literature were to
monitor cavity pressure independently of the internally mounted cavity
pressure sensor in some experiments and more densely sample at water vapor
mole fractions below 0.5 % H2O. Experiments were performed with five
Picarro GHG analyzers, henceforth labeled “Picarro nos. 1–5”, and
one Picarro oxygen analyzer labeled “Picarro no. 6”
(Table 1). The setup varied among experiments
(Table 1, Figs. 1–3) because of analyzer type (see Sect. 2.1 for a brief explanation) and because
experiments were performed at different stages of this study with different
goals (see caption of Table 1). In the following
sections, we first describe relevant aspects of the measurement principle
and hardware of Picarro analyzers and then describe our experiments.
Overview of experiments performed for this study. Experiments with
Picarro nos. 1 and 2 were conducted at an early stage of this work and
were designed to solely characterize the cavity pressure dependence on water
vapor. Therefore, the experiments with stable H2O levels with these
analyzers did not yield trace gas readings suitable for analysis (column 5).
Experiments with Picarro nos. 4 and 5 were performed without independent
pressure monitoring for reasons stated below. Spectroscopic cavity pressure
measurements were not possible with Picarro GHG analyzers (see Sect. 2.3.2).
LabelPicarroPicarroDropletStable H2O level experiment: externalStable H2O level experiment:analyzeranalyzerexperiment withcavity pressure measurement/usablespectroscopic cavitymodeltypeexternal pressuretrace gas measurements (reason)pressure measurementsmeasurementNo. 1G2401-mFlight-readyYesYes/no (used ambient air)NoNo. 2G2401RegularNoYes/no (disregarded equilibration)NoNo. 3G2401-mFlight-readyNoYes/yesNoNo. 4G2401-mFlight-readyNoNo (conducted before cavity pressureNohypothesis was developed)/yesNo. 5G2301RegularNoNo (remote field site)/yesNoNo. 6G2207-iRegularNoNo (replaced by spectroscopicYesmeasurements)/No (analyzer measures oxygen,not CO2 and CH4)Picarro GHG analyzers: measurement principle and active cavity pressure
stabilization system
Picarro GHG analyzers are based on the cavity ring-down spectroscopy method
(Crosson, 2008). In a measurement cavity, laser
pulses scan absorption lines of the target gases. The time it takes the
pulses to attenuate is converted to mole fractions of the gases. Among other
requirements, the analysis assumes stable pressure inside the measurement
cavity. Cavity pressure stability is achieved by a feedback loop (e.g.,
Fig. 1) between a pressure sensor (General Electric
NPC-1210) that is mounted inside the cavity and the outlet valve of the
cavity (inlet valve in so-called flight-ready Picarro GHG analyzers, which
are customized for airborne measurements). This loop keeps readings of the
cavity pressure sensor stable. Picarro GHG analyzers for CO2 and
CH4 used in this study, i.e., model series G2301 and G2401, operate at
186.65 hPa (140 Torr) with a 1σ tolerance of 0.20 hPa.
Setups for humidification
To humidify the air stream, two different methods were used. The first
approach was designed to maintain stable water vapor levels, while the
second approach was the commonly used droplet method. In this section, we
describe the experimental setup for both methods.
Stable water vapor levels
To create an air stream with stable water vapor levels, the dry air stream
was split into two lines, one of which remained untreated. Air in the other
line was directed through a gas washing bottle that contained deionized
water (e.g., Fig. 1). For experiments where CO2
and CH4 data were analyzed, the amount of water used was 15 mL (Picarro
no. 3) or 40 mL (Picarro nos. 4 and 5). With this method, air in the
humidified line was saturated with water vapor (mole fraction
∼3 % H2O). Subsequently, the two lines were joined
again. The water vapor mole fraction in the rejoined line was controlled by
adjusting the flow through the wet and dry lines. In the experiments with
Picarro nos. 1–5, this was achieved using needle valves; in the
experiment with Picarro no. 6, mass flow controllers (Alicat Scientific,
Tucson, Arizona) were used. In an experiment with Picarro no. 1 that was
conducted at an early stage of this work, instead of using the gas washing
bottle approach, stable water vapor levels were realized by mixing air from
the gas tank with ambient laboratory air. The experiment solely served to
characterize the cavity pressure dependence on water vapor; CO2 and
CH4 readings from this experiment were not analyzed.
Droplet method
For droplet experiments, the humidification unit described above was
replaced with a tee piece that enabled the injection of water droplets into the dry
air stream (Fig. 2).
Setups for cavity pressure monitoring
We used two methods to monitor pressure inside the measurement cavity
independently of the internally mounted pressure sensor. The first method
was based on an additional pressure sensor. Due to the complexity of this
setup, we developed a second cavity pressure monitoring method, based on
spectroscopic measurements, to verify the results of the first approach. In
this section, we describe the experimental setups for both methods.
Cavity pressure monitoring with external sensor
For this approach, cavity pressure was monitored with an additional pressure
sensor (General Electric Druck DPI 142). The optimal placement of this
sensor would be between cavity and inlet or outlet valve, as this position
would expose it directly to cavity pressure changes. However, opening tubing
connections at these positions would risk contaminating the cavity, which
would be expensive and time-consuming to fix. In addition, this setup could
interfere with temperature control of the cavity by introducing a heat
bridge and may thus require modifying the Picarro analyzer. For these
reasons, the external pressure sensor was installed outside of the Picarro
analyzer (e.g., Fig. 1). To ensure that the external
sensor could react to changes in cavity pressure, it was installed adjacent
to the cavity valve that was not used to control cavity pressure, i.e.,
upstream of the inlet valve in experiments with “regular” analyzers
(Fig. 1) and downstream of the outlet valve in
experiments with “flight-ready” analyzers (Fig. 2). During normal operation, the inlet and outlet valves act as chokes and
would thus shield the external pressure sensor from cavity pressure changes.
Therefore, pressure in the external pressure measurement branch was adjusted
to within a few hectopascals of cavity pressure by installing a needle valve as a
choke (e.g., Fig. 1). This way, the valve between
cavity and external pressure sensor did not act as a choke and the sensor
could react to cavity pressure changes. Since the external pressure sensor
may itself be sensitive to water vapor, it was shielded from humidity
changes by installing it behind a drying cartridge filled with magnesium
perchlorate in a dead end (e.g., Fig. 1). This setup
allowed us to monitor cavity pressure independently of water vapor content,
while the internal cavity pressure sensor still reacted to changes in water
vapor levels in the sampling air. The relationship between readings of the
external pressure sensor and cavity pressure changes was calibrated in
separate experiments with constant humidity (Sect. 2.4).
Cavity pressure monitoring with spectroscopic methods
Cavity pressure of Picarro analyzers affects the width of absorption lines
used to measure target gas mole fractions, and the optical phase length
(physical path length times refractive index) of the measurement cavity.
Both quantities were used to monitor cavity pressure.
The CO2 absorption line is not a good choice for this experiment
because it has a strong line broadening effect with water vapor
(Chen et al., 2010).
The CH4 absorption feature is also a poor choice because it is not a
clean, isolated line. Instead, a cavity ring-down spectroscopy analyzer measuring O2, δ18O, and H2O (G2207-i, Picarro, Inc., Santa Clara), which works
with an O2 absorption line at 7878.805547 cm-1 (John Hoffnagle,
personal communication, 2018), was used. The active cavity pressure stabilization
system of this analyzer is identical to that of Picarro GHG analyzers with
the exception that it operates at 339.97 hPa (255 Torr) rather than 186.65 hPa. Therefore, we expect the dependence of cavity pressure on water vapor
of this analyzer to be of similar magnitude and form as for GHG analyzers.
Both O2 line width and optical phase length are also influenced
directly by water vapor: pressure broadening of absorption line widths has
been shown in a variety of systems to be linearly dependent upon the
background gas matrix, and in particular on water vapor
(Chen
et al., 2010; Johnson and Rella, 2017; Nara et al., 2012). We therefore
expect a linear dependence of the O2 line width on water vapor mole
fraction. Similarly, the index of refraction of air also depends on the gas
matrix (Chen et al., 2016), leading to a linear dependence of
the optical phase length on water vapor mole fraction. Hence, we attribute
nonlinear dependencies of O2 line width and optical phase length on
water vapor to changes in cavity pressure.
Experiments for inferring sensitivities to varying cavity
pressure
To determine how readings of the external pressure sensor; CO2,
CH4, and H2O of the Picarro GHG analyzers; and O2 line width
and optical phase length of the oxygen analyzer react to changes in internal
cavity pressure, calibration experiments were performed. For these
experiments, air from a gas tank was measured with the Picarro analyzer.
Initial equilibration periods of readings from the external pressure sensor,
of
CO2 and CH4 (GHG analyzers), and of O2 line width and optical
phase length (oxygen analyzer) were discarded. Then, cavity pressure was
varied using Picarro Inc. software. Cavity pressure levels were chosen so
that the range spanned between dry and humid air as retrieved with the
external pressure sensor in water correction experiments was covered, and
they were probed for several minutes each. Most sensitivity experiments were performed
with dry air. With Picarro no. 3, an additional sensitivity experiment was
performed at a water vapor level of 3 % H2O. With Picarro nos. 4 and
5, no sensitivity tests were performed because no experiments with
external pressure monitoring were performed with these analyzers. This was
because the experiments with Picarro no. 4 were performed before the cavity
pressure hypothesis was developed, and Picarro no. 5 was operated at a
remote field site.
Water correction experiments with external pressure monitoringExperiments with stable water vapor levels
During stable water vapor level experiments with external pressure
monitoring, water vapor levels were probed between 15 and 150 min
(median about 40 min) depending on the stability of the external
pressure measurement and trace gas readings. External pressure readings
drifted on a timescale of several hours relative to internal cavity pressure
readings. Therefore, external pressure sensor readings obtained in humid air
were calibrated against external pressure sensor readings in dry air by
probing dry air before and after each measurement in humid air. For further
analysis, average readings from the Picarro GHG analyzer and the external
pressure sensor of the last 10 min of each probing interval were used to
reduce noise (15 min during the experiment with Picarro no. 3 and 5 min for some low water vapor levels with Picarro no. 1). The order of
water vapor levels was altered among experiments, including
high–low–high patterns and random alternations. Varying water levels
monotonically throughout an experiment was avoided to ensure that the
influence of various potential error sources was not systematic (Sect. S3).
Experimental setup for experiments with stable water vapor levels
and external pressure monitoring. Shown here is the setup for a regular
Picarro GHG analyzer (Picarro no. 2), from which only pressure data were
analyzed. For flight-ready analyzers, the external pressure measurement unit
was placed downstream of the analyzer (Fig. 2).
Droplet experiments
Droplet experiments with external pressure monitoring were performed with
Picarro no. 1 using the setup shown in Fig. 2. For
each droplet experiment, the tee piece was opened, a droplet of deionized
water (∼1 mL) was injected using a syringe, and the tee piece
was closed. Gradual evaporation of this water droplet then caused a gradient
over time from high to low water vapor levels in the sample air.
Experimental setup for water correction experiments with
humidification via water droplets and external pressure monitoring. Here,
the setup for a flight-ready analyzer is shown.
Experiments for spectroscopic cavity pressure measurements
For spectroscopic cavity pressure measurements, water vapor was ramped up
and down with a period of about 240 min for several cycles using the
setup depicted in Fig. 3. Two ranges of water vapor
mole fractions were selected for the experiment: a narrow range (0 % H2O–0.2 % H2O) for sampling the pressure bend at high resolution for five cycles
and a wider range up to about 0.8 % H2O for another six cycles to
establish the transition to a linear dependence of the pressure proxies
O2 line width and optical phase length on water vapor mole fraction.
Experimental setup for spectroscopic cavity pressure measurements.
Results
In this section, we first demonstrate the relevance of cavity pressure for
CO2 and CH4 measurements performed with Picarro GHG analyzers and
establish the sensitivities of the independent pressure monitoring methods
to changes in cavity pressure (Sect. 3.1). We then
present our results on the dependency of cavity pressure on water vapor
(Sect. 3.2) and introduce modifications to the
standard water correction model for CO2 and CH4 that account for
this sensitivity (Sect. 3.3). Finally, we examine
the performance of standard and modified water correction models in water
correction experiments with stable water vapor levels (Sect. 3.4) and droplet experiments
(Sect. 3.5).
Sensitivities of independent pressure measurements and trace gas readings to
changes of internal cavity pressure
In the sensitivity tests with Picarro GHG analyzers, readings from the
external pressure sensor, as well as of CO2 and CH4, all varied
linearly with cavity pressure, demonstrating that biases in cavity pressure
directly affect mole fraction readings. Similar sensitivities were observed
for all analyzers (Table 2). On average, for dry
air mole fractions of 400 ppm CO2 and 2000 ppb CH4, a change of 1 hPa in cavity pressure would cause a
difference of 0.37 ppm CO2 and 6.4 ppb CH4. The sensitivities obtained in the experiment with humid air
(3 % H2O) differed by only a few percent from those obtained in dry air
with the same analyzer (CO2: +5 %, CH4: -2 %, external
pressure readings: -1 %). Hence, all sensitivities were treated as
independent of the water vapor mole fraction.
In the sensitivity tests with the oxygen analyzer, both the O2 line
width and the optical phase length of the cavity varied linearly with cavity
pressure, with the sensitivities shown in Table 2.
Sensitivities of readings of Picarro GHG analyzers and independent
pressure measurements to variations in internal cavity pressure
p. For the quantities pertaining to GHG analyzers, averages and
standard deviations of all sensitivity experiments are reported, while for
the quantities pertaining to the O2 analyzer, mean and standard error
of the fit of the single experiment are given.
QuantityAnalyzerSensitivity to cavity pressureExternal pressure measurement ∂pext∂pnos. 1–3(0.95±0.04) hPa hPa-1CO2∂CO2∂p/CO2drynos. 1–3(9.2±0.3)×10-4 hPa-1CH4∂CH4∂p/CH4drynos. 1–3(3.22±0.05)×10-3 hPa-1O2 line widthno. 6(4.05±0.05)×10-3 hPa-1Optical phase lengthno. 6(163±3) nm hPa-1Dependency of cavity pressure on water vaporResults from external pressure sensor (stable water vapor
levels)Experimental results
Cavity pressure was monitored with the external sensor during experiments
with stable water vapor levels with three different Picarro GHG analyzers.
Readings of the internally mounted cavity pressure sensors were, owing to
the active pressure stabilization system of the analyzers, stable at 186.65 hPa with standard deviations of 0.02 hPa or less (as expected). However,
cavity pressure as estimated based on external pressure sensor readings and
their sensitivity to cavity pressure variations (Sect. 3.1) varied systematically with the water vapor
mole fraction, revealing that the readings of the internal sensors were
biased in the presence of water vapor. Cavity pressure estimated based on
the external sensor displayed a uniform pattern for all three analyzers
(Fig. 4): cavity pressure decreased when the water
vapor level increased, and the gradient of the variation was larger below
about 0.2 % H2O, which created a bend in the dependency of cavity
pressure on water vapor (henceforth called “pressure bend”).
Cavity pressure estimated based on external pressure sensor readings
in experiments with stable water vapor levels and fits of the empirical
cavity pressure model Eq. (4) to the data. Error bars: lower bound of
uncertainty; see Sect. S1.2 in the Supplement.
Empirical description
Based on these results, we formulated an empirical description of cavity
pressure dependency on water vapor:
pesth=p0+s⋅h+dp⋅e-hhp-1.
In this equation, pest is the estimated cavity pressure, h is the
water vapor mole fraction, p0 is the cavity pressure in dry air (186.65 hPa for Picarro GHG analyzers), hp is the position of the pressure
bend, s is the slope for h≫hp, and dp describes the magnitude
of the pressure bend.
The empirical cavity pressure model Eq. (4) was
fitted to the data of each analyzer. The coefficient of determination was
larger than 0.98 for all experiments, indicating good fits. Estimated
coefficients varied among analyzers (Table 3).
Coefficients of the empirical cavity pressure model Eq. (4) for data from experiments with stable water
vapor levels and external pressure monitoring (estimate and standard error).
The last line shows averages and standard deviations of the individual
estimates.
Analyzers (hPa (% H2O)-1)hp (% H2O)dp (hPa)No. 1-0.131±0.0090.066±0.0090.245±0.016No. 2-0.106±0.0030.076±0.0090.193±0.009No. 3-0.057±0.0040.095±0.0110.286±0.012Average-0.10±0.040.079±0.0140.24±0.05Results from the external pressure sensor during droplet experiments
Cavity pressure estimated based on external pressure sensor readings varied
strongly among droplet experiments and was consistently lower than during
the stable water vapor level experiment with this analyzer
(Fig. 5a). The largest variations
occurred below 1 % H2O. In this domain, the droplets dried up
quickly, which caused very fast decreases of the water vapor mole fraction
from about 0.5 % H2O–1 % H2O to 0 % H2O (Fig. 5b).
(a) Cavity pressure during droplet experiments with Picarro no. 1
estimated based on data from the external pressure sensor. For reference,
the results from the experiment with stable water vapor levels from this
analyzer are plotted as well (same as in Fig. 4).
(b) Temporal progression of water vapor mole fraction during the droplet
experiments after the drop below 3.5 % H2O.
Results from spectroscopic cavity pressure measurements
In the experiment with the oxygen analyzer (Sect. 2.6), O2 line width measurements obtained for
the same humidity levels throughout all cycles were stable (not shown). To
reduce their noise, they were averaged over periods of 100 s. By
contrast, the optical phase length of the cavity drifted over the course of
the experiment (explained in Sect. S2). Therefore, the averaged data based
on the phase length were binned for further analysis, separately for the
cycles between 0 % H2O and 0.2 % H2O and those between 0 % H2O and 0.8 % H2O.
At water vapor mole fractions above 0.2 % H2O, cavity pressure
estimates based on optical phase length and O2 line width both showed
linear dependencies on water vapor, potentially with a small nonlinear
component in the O2 line width data (Fig. 6).
The linear dependencies can be ignored here, as they are compounded by
effects other than cavity pressure changes (Sect. 2.3.2). Below about 0.2 % H2O, both
estimates exhibited the pressure bend that was also observed with the
external pressure sensor. Fitting the empirical cavity pressure model Eq. (4) yielded coefficients for pressure bend position
and magnitude very similar to those derived from data of the external
pressure sensor (Table 4) and coefficients of
determination larger than 0.98, which indicates good fits.
Coefficients for the empirical cavity pressure model Eq. (4) based on spectroscopic methods (estimates and
standard errors). The last line shows averages and, as uncertainty, half the
spreads of the individual estimates. The average of the slopes is not given
because the slopes are caused by different physical processes.
Methods (hPa (% H2O)-1)hp (% H2O)dp (hPa)O2 line width0.443±0.0020.076±0.0020.221±0.002Optical phase length-0.38±0.020.078±0.0190.222±0.024Average–0.0767±0.00080.2216±0.0006Modification of standard water correction model to account for cavity
pressure sensitivity to water vapor
Based on the results from sensitivity experiments and independent cavity
pressure measurements, the standard water correction model Eq. (3) was modified to account for cavity pressure
sensitivity to water vapor. First, the impact of measured deviations of
cavity pressure from its nominal value (Δp=p-p0) was
subtracted from the wet air mole fractions. Then, the standard water
correction model was applied to the modified wet air mole fractions:
cdrypressurecorrection=cweth-∂c∂p⋅Δpfcparah.
Here, ∂c∂p is the sensitivity
of the trace gas to cavity pressure changes. Henceforth, we call this the
“pressure correction” model.
The pressure correction model requires independent measurements of cavity
pressure. To eliminate the need for such measurements, the model was
reformulated based on the empirical pressure correction model by
substituting Δp in Eq. (5) with
pest-p0 from Eq. (4) and
rearranging the terms, which yields
cdryexpanded=cwethfcexph,
with an expanded water correction function fcexph:
fcexph=1+ac⋅h+bc⋅h2︸fcpara(h)+dc⋅e-hhp-1.
Here, hp is the pressure bend position from Eq. (4), and dc=dp⋅∂c∂p. Possible sensitivity of ∂c∂p to water vapor, which was not detected in
sensitivity experiments (Sect. 3.1), was neglected
here. Coefficients for this model can be estimated from trace gas data; i.e.,
independent cavity pressure measurements are not needed.
Cavity pressure estimated based on spectroscopic pressure
measurements with Picarro no. 6 and fits of Eq. (4). Error bars of O2 line widths and optical
phase lengths are the standard errors of averaging and binning,
respectively. Since the cycles up to 0.2 % H2O did not extend into
the linear domain, the model was not fitted to the optical phase length data
of these cycles. The slopes of the linear parts of the curves are compounded
by effects other than cavity pressure variations (see Sect. 2.3.2).
Water corrections based on experiments with stable water vapor
levelsExperiment with external pressure measurement
In this section, we show biases of the standard water correction model and
link them to the cavity pressure sensitivity to water vapor. For this
purpose, we collected data for both cavity pressure and the target gases
CO2 and CH4 in one stable water vapor level experiment (with
Picarro no. 3). We compare dry air mole fractions based on the standard,
pressure correction, and expanded water correction models (Eqs. 3, 5, and 6, respectively). In Fig. 7, we present
dry air mole fractions alongside the WMO internal
reproducibility goals. This context was chosen because, as stated in Sect. 1, keeping the bias of an individual measurement
system between calibration scale and measurement within these goals ensures
achieving the interlaboratory compatibility goals.
Dry air mole fractions from the experiment with Picarro no. 3 based
on the standard water correction model, pressure correction model (i.e., using
independently measured cavity pressure), and expanded water correction model
(i.e., using the empirical dependence of cavity pressure on water vapor).
Error bars show 1 standard deviation of the trace gas mole fractions measured
in dry air. The solid lines are the biases of the models assuming the
expanded model was unbiased (smoothed for the pressure correction model),
offset by the mole fractions measured in dry air. The upper and lower dashed
lines correspond to the WMO internal reproducibility goals (see Sect. 3.4.1), in the case of CO2 in the Northern
Hemisphere (WMO, 2016).
Dry air mole fractions of CH4 calculated using the standard water
correction model had a water-dependent structure
(Fig. 7, bottom panel), with sustained negative
biases at water vapor levels below 1 % H2O as the most prominent
feature. This structure was eliminated by the pressure correction model and the
expanded model, so that the dry air mole fractions based on these models
varied less (Table 5). The largest difference
between the standard and expanded water correction models occurred at 0.2 % H2O (Table 6). Differences
between the
pressure correction and expanded models were small
(Fig. 7, bottom panel).
For CO2, dry air mole fractions based on the standard model had a
structure similar to that of the CH4 mole fractions, but the differences to the
expanded water correction model, which performed best, were much smaller
than for CH4 in terms of the overall variability
(Table 5) and compared to the WMO internal
reproducibility goals in the Northern Hemisphere
(Fig. 7, top panel, and
Table 6). The pressure correction model showed a
comparatively poor performance, dominated by a small bias similar to the one
present in the results of the standard model but with the opposite sign
(Fig. 7, top panel).
Standard deviations of dry air mole fractions based on different
water correction models from the experiment with Picarro no. 3.
Maximum differences between dry air mole fractions based on the
standard and expanded water correction models from the experiment with
Picarro no. 3. The largest differences are also given as percentages of the
mole fractions measured in dry air.
CO2CH4PositionNegative0.023 ppm/0.86 ppb/0.2 % H2O0.006 %0.047 %Positive0.011 ppm0.41 ppb1.7 % H2ORange0.034 ppm1.27 ppbVariability among experiments with the same analyzer
With Picarro no. 5, one gas washing bottle experiment was performed in
2015 and 2017 each, without external cavity pressure monitoring. In the 2015
experiment, the number of data points was insufficient to fully constrain
both hp and dc in the expanded water correction model. Since the
(uncertain) estimate of hp based on CH4 was close to the mean of
hp from the three experiments with external cavity pressure monitoring
(hpmean=0.079±0.014%H2O), hp was set to hpmean for
this experiment. We also considered using hp from the 2017 experiment
instead, but this induced biases in water-corrected CH4 mole fractions.
For the 2017 experiment, the estimate of hp based on CH4 data was
also used for CO2 because its estimate based on CO2 data was
highly uncertain.
Water-corrected dry air mole fractions from the two experiments
with Picarro no. 5. The data from the 2015 experiment have been scaled up to
match the mole fractions measured in dry air in the 2017 experiment. The
points are based on model fits to data from both experiments jointly (error
bars: lower bounds of uncertainty; see Sect. S3), while the solid lines show
differences between the standard and expanded water correction models fitted to
data from the 2015 and the 2017 experiments individually, offset by the mole
fractions measured in dry air in the 2017 experiment. The dashed lines are
the same as in Fig. 7.
For both experiments, dry air mole fractions of CO2 and CH4
obtained using the standard water correction model had negative biases
around the pressure bend position and at the highest sampled water vapor
mole fractions (3 % H2O) and a positive bias in between (lines in
Fig. 8, Table 7). The
biases were eliminated by the expanded model (Table 7). The magnitudes of the biases of water-corrected CO2 mole fractions
were consistent with those of CH4. In the 2015 experiment, the largest
bias occurred around the pressure bend position, while in the 2017
experiment, the largest positive biases, which occurred at 1.9 % H2O, and the negative biases at the highest sampled water vapor mole
fractions were on par with those at the pressure bend position
(Table 7). Residuals were much larger than the
estimated lower bounds of the uncertainty (error bars in
Fig. 8), owing to the fact that not all
uncertainties could be quantified (Sect. S3).
Comparison of water corrections of the two experiments with Picarro
no. 5. The bias estimates of the standard model are based on the assumption
that the results of the expanded model were unbiased.
CO2CH42015 experiment2017 experiment2015 experiment2017 experimentCoefficients (mean ± SE) (individual experiments) hpSee CH4See CH4(0.079±0.014) % H2O(0.26±0.06) % H2O(from Table 3)dc(1.6±0.3)×10-4(3.0±0.6)×10-4(6.6±1.1)×10-4(1.7±0.1)×10-3Coefficients (mean ± SE) (joint correction with data from both experiments) hpSee CH4(0.16±0.04) % H2Odc(2.3±0.4)×10-4(1.19±0.08)×10-3Standard deviations (individual experiments and joint correction) Standard model0.02 ppm0.04 ppm0.39 ppb0.7 ppbExpanded model0.01 ppm0.02 ppm0.17 ppb0.2 ppbExpanded model0.016 ppm0.027 ppm0.24 ppb0.23 ppb(joint correction)Maximum biases of the standard model assuming the expanded model was unbiased (individual experiments) Negative, position0.037 ppm/0.0104 %,0.041 ppm/0.0105 %,0.78 ppb/0.0437 %,1.07 ppb/0.0545 %,(< 1 % H2O)0.18 % H2O0.32 % H2O0.18 % H2O0.32 % H2OPositive, position0.015 ppm, 1.8 % H2O0.043 ppm, 1.9 % H2O0.32 ppb, 1.8 % H2O1.10 ppb, 1.9 % H2OMaximum differences by swapping coefficients of expanded model between individual experiments < 1 % H2O0.02 ppm/0.049 % 0.6 ppb/0.030 % >3 % H2O0.07 ppm/0.018 % 0.4 ppb 0.022 %
The water correction coefficients obtained from the two experiments had
significant differences (Table 7). To assess the
impact of these differences on water-corrected dry air mole fractions, two
analyses were performed. First, the coefficients of either experiment were
applied to the other one. This resulted in differences around the pressure
bend positions, but they were smaller than the differences between the standard
and expanded water correction models. In addition, CO2 differed at the
largest water vapor mole fraction sampled (Fig. 8,
top panel; Table 7). For a second assessment of
differences between the two experiments, the 2015 data were scaled up to the
mole fractions measured in dry air in the 2017 experiment and the expanded
model was fitted to all data to obtain joint water corrections (points in
Fig. 8). Standard deviations of the water-corrected
dry air mole fractions based on the joint correction were between those
based on the individual standard and expanded models
(Table 7).
A case without bias of the standard water correction model
With Picarro no. 4, a gas washing bottle experiment without independent
cavity pressure monitoring was performed. Dry air mole fractions obtained
with the standard water correction model did not exhibit the systematic
biases observed in Picarro nos. 3 and 5 (Fig. 9) and had standard deviations of 0.016 ppm CO2 and 0.21 ppb CH4.
This is better than the performance of the standard model in the experiments
with the other analyzers, and for CH4 close to the performances of the
expanded model. Applying the expanded model to these data yielded
insignificantly small pressure bend magnitudes dc and thus very similar
dry air mole fractions without improvement of the variability (not shown).
Residuals were much larger than the estimated lower bounds of the
uncertainty (error bars in Fig. 9), owing to the
fact that not all uncertainties could be quantified (Sect. S3).
Dry air mole fractions of CO2 and CH4 for a gas washing
bottle experiment with Picarro no. 4 based on the standard water correction
model. Error bars: lower bound of uncertainty; see Sect. S3. The dashed
lines are the same as in Fig. 7.
Water corrections based on droplet experiments
The water correction models were fitted to the data from droplet experiments.
The data were filtered for water vapor mole fractions below 3.5 % H2O and
for differences among subsequent H2O measurements of less than
0.005 % H2O. The former filter ensured compatibility with the gas
washing bottle experiments, while the latter was an empirical filter to
exclude the fastest water vapor variations, which resulted in large
variations in CO2 and CH4 readings, while leaving enough data for
fitting.
Dry air mole fractions obtained with the standard water correction model had
the typical bias structure that was also observed during gas washing bottle
experiments (compare Fig. 10 with Figs. 7 and 8). Both
the pressure correction and the expanded models reduced or eliminated the
biases induced by the standard model, with better performance of the
pressure correction model (Table 8). While the
CH4 bias at low water vapor mole fractions was eliminated by the
pressure correction model, the bias of CO2 was only reduced.
Dry air mole fractions from droplet experiment 1 with Picarro
no. 1 based on the three water correction models. Droplet 1 is shown because
it yielded the most data points after applying the filters described in the
text. The dashed lines are the same as in Fig. 7.
Average standard deviations of dry air mole fractions from all
droplet experiments with Picarro no. 1 based on the three water correction
models.
During the fast decreases in water vapor mole fractions from about 0.5 %–1 % to 0 % H2O (Sect. 3.2.2), differences
between wet air mole fractions among droplet experiments were large. The
differences were quantified based on fitting the water correction functions
of all models to wet air mole fractions from the individual droplets. The
expanded function captured the large differences, which were up to 0.17 ppm
CO2 and 6.0 ppb CH4 (Fig. 11). By
contrast, differences among fits of the parabolic water correction
function to wet air mole fractions (standard model), as well as to
pressure-corrected wet air mole fractions (pressure correction model), were
much smaller, i.e., 0.04 ppm CO2 and 0.8 ppb CH4 (not shown).
DiscussionFindings from sensitivity experiments
Sensitivity experiments revealed sensitivities of CO2 and CH4
readings of Picarro GHG analyzers to cavity pressure. This demonstrates that
trace gas readings are affected by systematic biases of cavity pressure.
Furthermore, these sensitivity experiments established the ability of our
independent cavity pressure monitoring methods to detect cavity pressure
changes. As a caveat, the sensitivity experiments did not characterize
potential direct sensitivities, unrelated to cavity pressure changes, of the
independent pressure monitoring methods to water vapor changes. For the
approach using the external pressure sensor, the experiments were designed
to prevent such sensitivity by installing the sensor behind a drying
cartridge and in a dead end. Nonetheless, several parts of the setup may
have caused a sensitivity of the readings of the external sensor to water
vapor changes (details are given in Sect. S1). In the approach using
spectroscopic pressure measurements, experiments with varying water vapor
indeed revealed linear dependencies on water vapor. Since their sign
differed, they must at least partly have been caused by effects other than
cavity pressure changes (Fig. 6). However, linear
dependencies of the independent pressure estimates on water vapor do not
affect our conclusions since they are covered by the water correction
models. The key result of our experiments, the pressure bend, was broadly
consistent among data from the external pressure sensor, both
spectroscopic cavity pressure estimates, and CH4 data. Given that all
of these quantities were estimated based on different, unrelated methods, it
is unlikely that our independent cavity pressure monitoring methods had
systematic, water-dependent biases that affected our conclusions.
Cavity pressure of Picarro analyzers is sensitive to water vapor
Results from all independent cavity pressure measurements demonstrate that
cavity pressure of Picarro analyzers is sensitive to the water vapor content
of the sample air. We described the sensitivity empirically based on the
results of experiments with stable water vapor levels and external cavity
pressure monitoring with Eq. (4).
Expanded water correction model fitted to data from four droplet
experiments with Picarro no. 1. To emphasize the large differences, a common
linear component has been subtracted. The dashed lines are the same as in
Fig. 7.
Results from either humidification method indicate that cavity pressure
takes time to adjust to new water vapor levels. To investigate whether
cavity pressure equilibration affected the conclusions drawn from water
correction experiments with stable water vapor levels, we inspected long
(5–12 h) measurements of dry air after switching from humid air for
evidence of cavity pressure equilibration longer than our typical probing
time of humid air (40 min) and found only small variations (Sect. S1.1).
We did not check for long equilibration after switching from dry to humid
air. However, in both gas washing bottle and droplet experiments, there
was no indication that cavity pressure equilibrated more slowly with
increasing than with decreasing water vapor mole fraction. Therefore, it is
unlikely that cavity pressure equilibration affected the conclusions drawn
from the experiments with stable water vapor levels.
Results from spectroscopic cavity pressure measurements agreed with the
results of the external pressure sensor. Both the estimate based on O2
line width and the one based on optical phase length exhibited the pressure
bend with the same sign, and at a position and magnitude close to the
average of the estimates based on the external pressure sensor. We note that
we expected the magnitude of the pressure bend to scale with cavity
pressure, i.e., that it would be larger than estimates based on GHG analyzers
by a factor of ≈1.8, the ratio of cavity pressures of these
instruments. Given the variability in dp among the three experiments
with GHG analyzers, it is not certain whether this was the case.
We speculate that the observed sensitivity of internal pressure readings to
humidity levels in sampled air is due to adsorption of H2O molecules on
the pressure sensor inside the cavity. The pressure measurement is based on
a piezoresistive strain gauge exposed to the pressure media (air in the
cavity). The strain gauge is mounted on a thin diaphragm, which is deflected
by pressure. The resulting strain causes a change in electrical resistance
and creates an output voltage varying with pressure. Water molecules
adsorbed on the strain gauge, diaphragm, or adjacent parts of the sensor may
change its response to pressure mechanically and/or may affect the
electrical properties of the circuit. However, elucidating the underlying
physical effect of the cavity pressure changes is beyond the scope of this
paper and was not investigated further.
Since CO2 and CH4 readings react to changes in cavity pressure,
the sensitivity of cavity pressure to water vapor affects CO2 and
CH4 readings in humid air. Therefore, the results on cavity pressure
imply that an adequate correction method is required to avoid systematic
biases in water-corrected dry air mole fractions of CO2 and CH4
due to the cavity pressure dependence on water vapor.
Cavity pressure sensitivity to water vapor causes biases in CO2 and
CH4 readings
Applying the standard water correction model resulted in biases in
water-corrected CO2 and CH4 mole fractions in experiments with
stable water vapor levels and droplet experiments. The shortcoming of the
standard water correction model is that it is unable to model the pressure
bend. The pressure correction model, which directly links independently
estimated cavity pressure to trace gas readings, eliminated the biases in
CH4 in all experiments. Although results for CO2 were mixed (see
Sect. 4.7), the performance of the
pressure correction model demonstrates a link between cavity pressure
sensitivity to water vapor and trace gas readings of Picarro GHG analyzers
in humid air. Biases of the standard model depend on the dry air mole
fraction and in our experiments amounted to up to 50 % of the WMO
interlaboratory compatibility goal for CH4 and 80 % of the goal for
CO2 in the Southern Hemisphere (Picarro no. 5, 2017 experiment).
Correcting for cavity pressure sensitivity to water vapor without
independent cavity pressure measurements
We developed the expanded water correction model to allow correction for the
sensitivity of cavity pressure to water vapor without independent cavity
pressure measurements. The model combined the parabolic water correction
model from the literature with our empirical description of the dependency
of cavity pressure on water vapor, which was composed of a linear term and
an exponential term describing the pressure bend. We note that O2 line
width data suggest a small curvature of the cavity pressure dependency
beyond the pressure bend (Fig. 6), as do data from
the external pressure sensor during droplet experiments at water vapor mole
fractions larger than those covered by our experiments with stable water
vapor levels (Fig. 5a). However, small
curvatures can be captured by the parabolic part of all models, implying the
expanded model is suitable despite potential shortcomings of the empirical
cavity pressure model it was based on.
Experiments with stable water vapor levels
In the water correction experiment with stable water vapor levels and
external cavity pressure monitoring, the CH4 results of the expanded
model closely matched those of the pressure correction model (Sect. 3.4). It also fitted the observed CO2 mole
fractions from this experiment well, but their inconsistency with data from
the external pressure sensor calls these CO2 data into question (Sect. 4.7). More water correction experiments with stable
water vapor levels were performed without independent cavity pressure
measurement. In these experiments, consistency with cavity pressure could
not be checked directly, but comparing the pressure bend magnitudes
dCO2 and dCH4, as well as estimates of hp based on
either trace gas, provides useful information on potential inconsistencies.
For instance, in the experiments with Picarro no. 5, dCO2 and
dCH4were broadly consistent (not shown), while in the experiment
with Picarro no. 3, dCO2 was smaller than expected. In conclusion,
CO2 and CH4 readings can be corrected for the dependency of cavity
pressure on water vapor based on experiments with stable water vapor levels
using the expanded water correction model, which does not require
independent cavity pressure monitoring. Water correction experiments need to
sample water vapor mole fractions between 0 % H2O and 0.5 % H2O
sufficiently densely to constrain the pressure bend.
Droplet experiments
During droplet experiments, cavity pressure depended on the temporal course
of water vapor variation. In particular, water vapor diminished quickly
around the pressure bend position, but with a different temporal course in
each experiment. Cavity pressure estimated based on the external pressure
sensor was lower than during the experiment with stable water vapor levels
and at the same time inconsistent around the pressure bend position, with
the slowest-evaporating droplet closest to the data from the experiment with
stable water vapor levels. This suggests that the fast water vapor
variations did not allow the measurements of the internal cavity pressure
sensor to equilibrate, which caused biased CO2 and CH4 readings.
While the biases were mitigated by the pressure correction model, applying
the expanded model yielded exaggerated and inconsistent pressure bends.
Therefore, the results of our droplet experiments proved unsuitable for
correcting cavity pressure-related biases of CO2 and CH4 readings
without independent cavity pressure monitoring. However, droplet 1
evaporated more slowly than the other droplets and the experiment yielded
cavity pressure data closer to those from the experiment with stable water
vapor levels. This experiment was performed on another day, and the setup
was reassembled in between. Thus, the course of evaporation may have been
affected by the length and shape of the tubing between the droplet injection
point and Picarro analyzer. Based on the results from this droplet, we
speculate that droplet experiments with even slower evaporation may yield
results from which coefficients for the expanded water correction model can
be derived.
Temporal stability of expanded water correction model
With Picarro no. 5, two experiments with stable water vapor levels were
performed 2 years apart. Coefficients of the expanded model differed
significantly between these experiments. It is unclear whether the
differences were due to limited reproducibility, short-term variations, or
long-term drifts, and more experiments are required to understand the
variability. Variability may also be caused by mechanisms other than the
sensitivity of cavity pressure to water vapor, which may explain the
differences at water vapor mole fractions well above the pressure bend
position. The differences around the pressure bend position between the two
experiments were smaller than biases of the standard model. Therefore, dry
air mole fractions in this domain based on either set of coefficients were
likely more accurate than those based on the standard model despite the
variation between the two experiments.
Differences of expanded water correction model among analyzers
In total, we performed water correction experiments with stable H2O
levels for CO2 and CH4 with three Picarro GHG analyzers. While the
position (hp) and magnitude (dc) of the pressure bend in CO2
and CH4 readings were broadly consistent between Picarro nos. 3 and
5 (with the exception that the effect on CO2 of Picarro no. 3
appeared reduced; see Sect. 4.7), CO2 and
CH4 readings from Picarro no. 4 exhibited no detectable pressure bend.
The magnitude of the pressure bend of this analyzer may be smaller than that
of the others, masked by random fluctuations, or not be present at all.
Alternatively, the pressure bend position may have been at a higher water
vapor level, so that the standard model could capture the bend. The
differences between this analyzer and the others are not explained by
estimated uncertainties (Sect. S3). Thus, they remain an open question for
future research. The differences imply that custom coefficients for the
expanded model should be obtained for each Picarro analyzer.
Challenges for CO2
In all water correction experiments with independent cavity pressure
monitoring, CO2 data were not fully consistent with independent cavity
pressure data. In the water correction experiment with stable water vapor
levels and external pressure monitoring (Picarro no. 3), biases of dry air
CO2 mole fractions obtained using the standard water correction model
were much smaller than expected from cavity pressure variations, i.e., the
pressure correction model overcompensated for the bias of the standard model. By
contrast, biases of dry air mole fractions of CO2 obtained using the
standard model based on data from droplet experiments were reduced by the
pressure correction model, but not fully eliminated. Since CH4 data
were consistent with data from the external pressure sensor (Sect. 4.3), the most likely cause for the mixed CO2
results is variations in the CO2 mole fractions delivered to the
analyzer. Since in all our water correction experiments the air stream was
in contact with liquid water, the underlying reason may have been
dissolution in and outgassing from these reservoirs. This would likely have
affected CO2 more than CH4 since its solubility in water is much
higher. During gas washing bottle experiments, we took this effect into
account by carefully observing the equilibration of trace gas mole fraction
readings. However, it is conceivable that our efforts were not sufficient.
If this explanation were true, the systematic difference between dry air and
wet air CO2 mole fractions in the experiment with Picarro no. 3 would
have precisely compensated for the pressure bend, which seems unlikely.
Therefore, we regard this interpretation with caution and acknowledge the
possibility that another mechanism caused the inconsistencies of CO2
readings with the data from the external pressure sensor (a more detailed
discussion can be found in Sect. S3). Overall, our results highlight the
need for high-quality data to correct CO2 readings for the effects of
water vapor.
Conclusions
We reported previously rarely detected and unexplained biases of CO2
and CH4 measurements obtained with Picarro GHG analyzers in humid air.
They were largest at low water vapor mole fractions below 0.5 % H2O,
where they amounted to up to 50 % (∼1 ppb) of the WMO
interlaboratory compatibility goal for CH4 and 80 %
(∼0.04 ppm) for CO2 in the Southern Hemisphere at
ambient mole fractions.
The biases may affect not only measurements without drying systems, but also
measurement systems that use Nafion membranes to dry air samples due to
residual water vapor. Stavert et al. (2018)
reported that in their setup, the Nafion membrane humidified calibration air
to less than 0.015 % H2O, while the humidity of the sample air was
on average 0.2 % H2O. This humidity difference could result in the
maximum biases we observed. Conversely, other studies reported
smaller differences between the water levels of sample and calibration air
after passing through Nafion
(Verhulst
et al., 2017; Welp et al., 2013). Eliminating differences between residual
water vapor levels of sample and calibration air would remove the biases
reported here, as would drying sample air to very low water levels, e.g.,
using a cryotrap.
The biases are due to a sensitivity of the pressure in the measurement
cavity to water vapor, which we observed with both an additional external
pressure sensor and based on spectroscopic methods. We speculate that the
underlying physical mechanism of the cavity pressure variability is
adsorption of water molecules on the piezoresistive pressure sensor in the
cavity that is used to keep cavity pressure stable.
The biases can be corrected without independent cavity pressure measurements
based on experiments with stable water vapor levels by an empirical
expansion of the standard water correction model from the literature, which
we derived from the cavity pressure dependency on water vapor.
Correction of the biases of CO2 readings was challenging, presumably
because of dissolution in and outgassing from the water reservoir used to
humidify the air stream.
The commonly used droplet method did not yield results suitable for
correcting biases of CO2 and CH4 readings related to cavity
pressure without independent cavity pressure monitoring. In these
experiments, water vapor varied faster than it takes cavity pressure to
adjust to a new water vapor level. We speculate that water droplets may
nonetheless be suitable for deriving coefficients for the expanded water
correction model under the condition that evaporation is sufficiently slow.
Since our results do not determine the necessary equilibration time, we
recommend using humidification methods that allow stable water
vapor levels to be maintained. Since the humidification via gas washing bottle is complicated
to implement in the field and may have affected our CO2 results,
alternative humidification methods may be more suitable. For example,
Winderlich et al. (2010)
achieved stable water vapor levels with much smaller amounts of liquid water
in the air stream using a so-called “water trap”, which is akin to a
droplet experiment with more controlled evaporation.
Future research is necessary to understand differences of cavity
pressure-related biases of CO2 and CH4 among analyzers and over
time. Therefore, coefficients for the expanded model should be obtained for
each analyzer individually and be monitored over time.
The biases addressed here are on the order of magnitude of the WMO
interlaboratory compatibility goals. They did not exceed them, but several
other error sources that affect GHG measurements, like tracing the
calibration of the gas analyzer to a common primary scale
(e.g.,
Andrews et al., 2014), are on the same order of magnitude. Therefore, to
reach the WMO interlaboratory compatibility goals, biases from each
individual error source need to be “as small as possible”
(Yver Kwok et al., 2015). Thus,
accounting for cavity pressure-related biases of CO2 and CH4
readings contributes to keeping the compatibility of measurements performed
with the widely used Picarro GHG analyzers in humid air and potentially in
Nafion-dried air within the WMO interlaboratory compatibility goals.
The data analyzed for this study are available upon request to the contact author.
The supplement related to this article is available online at: https://doi.org/10.5194/amt-12-1013-2019-supplement.
FR, CG, JL, and MG conceptualized the study. FR, CG, JL, and CR designed the experiments.
FR and CR performed experiments and analyzed the data. FR prepared the original and revised drafts of the paper with contributions from all
authors. MG supervised the study and reviewed the different versions of the paper.
Chris W. Rella is an employee of Picarro, Inc.
Acknowledgements
This work was supported by the Max-Planck Society, the European Commission
(PAGE21 project, FP7-ENV-2011, grant agreement no. 282700; PerCCOM project,
FP7-PEOPLE-2012-CIG, grant agreement no. PCIG12-GA-201-333796; INTAROS
project, EU-H2020-BG-09-2016, grant agreement no. 727890), the German
Ministry of Education and Research (CarboPerm project, BMBF grant no.
03G0836G), the AXA Research Fund (PDOC_2012_W2
campaign, ARF fellowship Mathias Göckede), and the European Science
Foundation (TTORCH Research Networking Programme, Short Visit Grant Friedemann Reum). We thank Stephan Baum, Dietrich Feist, and Steffen Knabe (MPI-BGC) for
help with the experiments. We thank David Hutcherson (Amphenol Thermometrics
(UK) Ltd) for clarifications regarding the piezoresistive pressure
measurement technique. We thank Andrew Durso, Dietrich Feist, and Martin Heimann (MPI-BGC) for feedback on the paper.
The article processing charges for this open-access publication were covered by the Max Planck Society.
Edited by: Dominik Brunner
Reviewed by: two anonymous referees
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