Introduction
The concentration of N2O in the atmosphere is rising and is of concern
as N2O has 298 times the global warming potential of CO2 (IPCC,
2013). Agricultural systems contribute a significant proportion of total
global N2O emissions due to microbial soil processes (Davidson, 2009).
The main drivers of microbial N2O emissions are soil conditions (e.g.,
soil oxygen, carbon, and nitrogen levels), but distal drivers such as
nitrogen fertilization, dry–wet, and freeze–thaw cycles also exert control
and result in N2O fluxes (FN2O) that are highly variable in time
(“hot moments”) and in space (“hot spots”; Groffman et al., 2009;
Molodovskaya et al., 2012). This sporadic nature of FN2O necessitates
continuous measurements covering areas large enough to capture the spatial
heterogeneity of the fluxes to sufficiently quantify total emissions from
agricultural systems (Flechard et al., 2007; Jones et al., 2011; Shurpali et
al., 2016). The eddy-covariance (EC) technique gives long-term, continuous,
and spatially integrated measurements, which can fully capture FN2O.
Advances in technology for N2O analyzers have increased the number of
long-term N2O measurement campaigns using EC methods (Mishurov and
Kiely, 2010; Molodovskaya et al., 2012; Merbold et al., 2014; Huang et al.,
2014; Rannik et al., 2015; Shurpali et al., 2016; Wang et al., 2016).
However, the application has been limited to sites with high-quality power
or short-term deployment. Locations with agricultural N2O emissions are
often rural areas where it may be impractical and expensive to install
permanent power infrastructure for long-term FN2O EC flux measurements.
Gas analyzers used in EC systems require fast responses to distinguish the
high frequencies of concentration fluctuations (McBean, 1972; Leuning and
Judd, 1996). High-frequency concentration fluctuations can be attenuated by
line averaging within the sample cell, as well as sample mixing within
various system components, such as intake tubing, dryers, filters, and rain
caps (Moore, 1986; Massman, 2000). Analyzer frequency response often
represents a significant proportion of the total high-frequency losses
(Horst, 1997; Kroon et al., 2010b), but these sampling system components may
also significantly degrade system frequency response (Aubinet et al., 2016).
Modern closed-path analyzers for CO2 EC fluxes achieve high-frequency
response with low-power pumps by using small sample cells (6 or 16 mL) that
operate near ambient pressure (Burba et al., 2010; Novick et al., 2013). The
volume of sampling system components is minimized to preserve the frequency
response without excessive flow restriction that would increase power
requirements for the pump or exceed the range of the sample-cell pressure
sensor (Aubinet et al., 2016; Ma et al., 2017).
In contrast, all N2O analyzers tested in a recent EC field
intercomparison have large sample cells (∼ 500 mL) that must
operate at significantly reduced pressure (50 to 120 mb), necessitating high
flow rates (12 to 17 L min-1; Rannik et al., 2015). The high flow rate
and low pressure require the use of a relatively high-power pump (500 to
1000 W). Reducing the volume of the sample cell and sampling system
components is a means of maintaining good frequency response at a lower flow
rate while reducing the power requirements of N2O EC systems. Using a
dryer on the intake line may degrade system frequency response but can
eliminate uncertainties associated with spectroscopic corrections for line
broadening due to water vapour (Neftel et al., 2010) and density corrections
(Webb et al., 1980). The typically high sample flow rates require the use of
multi-tube dryers to achieve an acceptable residual humidity. However,
laminar flow within the dryer degrades system frequency response because air
velocity varies from the centre to the wall of the tube (Leuning and Judd,
1996), and multi-tube dryers can further degrade frequency response if the
flow varies among the individual tubes. This problem cannot be overcome by
simply increasing flow because a higher flow would require a larger dryer.
Alternatively, analyzers with smaller sample cells require lower sample
flow, allowing the use of single-tube dryers and lower-powered pumps to
achieve the frequency response required for EC measurements.
An upgraded version of a first-generation tunable diode laser absorption
spectrometer (TDLAS; Edwards et al., 2003) has recently been made
commercially available (TGA200A, Campbell Scientific Inc., Logan, Utah,
USA). The previous model was suitable for long-term N2O flux
measurements for a variety of agricultural and forested sites (Scanlon and
Kiely, 2003; Pihlatie et al., 2005; Pattey et al., 2006; Mishurov and Kiely,
2010; Molodovskaya et al., 2012). The upgraded model has been designed to
reduce power consumption of EC measurement systems by reducing the volume of
the sample cell. This was done without changing the path length, thereby
preserving measurement precision. The smaller sample cell allows a lower
flow rate, enabling the use of a single-tube dryer that maintains frequency
response better than high-capacity, multi-tube dryers. Adapting a new vortex
intake – originally developed for use with a CO2/H2O EC analyzer
(Ma et al., 2017) – to the N2O analyzer should further improve the
frequency response as this device removes particulates from the air stream
without adding a significant mixing volume. The vortex intake has the
additional advantage of not requiring a traditional filter that can clog
over time, restricting flow to the analyzer, and requiring maintenance. This
is an important aspect of EC system design for long-term studies because, as
stated by Nelson et al. (2004), all components need to be sufficiently
rugged to withstand exposure to environmental conditions and run reliably
for long time periods with minimal maintenance.
Cutoff frequencies are often used to characterize the frequency response of
EC analyzers and systems (McBean, 1972; Nelson et al., 2004; Detto et al.,
2011; Rannik et al., 2015) and for correcting flux values for high-frequency
losses (Moore, 1986; Horst, 1997; Aubinet et al., 2000; Massman, 2000;
Massman and Lee, 2002). Determining frequency response using field-measured
cospectra (Aubinet et al., 2000; Ibrom et al., 2007) is challenging for
N2O EC systems as this requires a strong concentration signal occurring
simultaneously with environmental conditions appropriate for scaling with
temperature spectra. Direct characterizations are more appropriate for
N2O analyzers since emission events may occur for only a few days in a
year, reducing the likelihood of ideal conditions for cospectral analyses.
Similarly, evaluating system lag times of closed-path N2O analyzers
used for EC flux processing with the standard cross-correlation method
requires a strong signal (Kroon et al., 2010b; Neftel et al., 2010).
Directly measuring lag times will give a better estimate of the tube delay
for periods with low N2O signals.
We present the first field trial of a new TDLAS with an optimized sampling
system, operating at lower flow rates with a lower-powered pump. Continuous
FN2O measurements are presented over two growing seasons for an
agricultural site in a cold climate. The system was evaluated for overall
performance of the instrument in terms of ease of operation, data quality,
and suitability for EC measurements. N2O fluxes measured by the new
TDLAS were compared to those measured by the previous-generation TDLAS and
CO2 fluxes (FCO2) were compared to fluxes measured by a short-tubed
CO2 analyzer. A novel method for determining EC system frequency
response and lag time was conducted in situ to determine the analyzer
frequency response under field operating conditions.
Descriptions of the field site and eddy-covariance
instrumentation. (a) Layout of the field site. Dashed lines indicate the
borders of the field treatments used in the larger four-plot flux gradient (FG)
study. Instrumentation for that study was housed in the indicated
instrumentation trailer located in the centre of the field. Solid lines
outline the entire field. (b) Wind rose of the wind speeds and direction
observed during the field study. (c) Configuration of the analyzers at the
EC tower; A is TDLAS-LN, B is TDLAS-TE, C is EC155, and D is reference gases for
the N2O analyzers. (d) Schematic of TDLAS-LN sampling system with
inline filter and multi-tube dryer. (e) Schematic of TDLAS-TE sampling
system with vortex intake and single-tube dryer.
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Methods
Study site
Flux measurements took place at the Elora Research Station, Elora, Ontario,
Canada (43.3∘27.8′ N, 80.24∘20.4′ W), from May 2015 to
October 2016 as a part of a four-plot flux-gradient study on the effect of
nitrogen fertilizer management practices on FN2O. The four 4 ha plots
were located within a larger 30 ha aerodynamically homogeneous and flat
field (Fig. 1a) under the same crop management practices. The soil at the
site was a Guelph silt loam (fine loamy, mixed, mesic Glossoboric Hapludalf). Corn was planted on 10
May 2015 and again on 5 May 2016. Urea fertilizer was broadcast-applied at
the time of planting in both years at a rate of 150 kg ha-1 to the plot
where instrumentation was set up (Fig. 1a). Corn grew to a maximum height of
approximately 2.5 m each summer. Harvest for 2015 started on 27 October and
completion was delayed due to rain until 2 November. The field was tilled
immediately afterwards, leaving a bare-soil surface from 2 November 2015 to
30 May 2016 when the corn emerged. Harvest in 2016 occurred on 7 October.
The field plot was located 400 m from an Environment Canada weather station
that measured supporting variables of mean wind direction, air temperature,
pressure, and precipitation.
Eddy-covariance measurements
An EC system (CPEC200, Campbell Scientific Inc., Logan, Utah, USA) was
installed in the southeast corner of the 4 ha plot (Fig. 1a, c) and was
comprised of a sonic anemometer (CSAT3A) to measure three-dimensional wind
and sonic temperature, a closed-path infrared gas analyzer (EC155) to
measure mixing ratios of CO2 and H2O, and a data logger (CR3000).
The sonic anemometer was oriented towards the predominant wind direction
(270∘; Fig. 1b). Two TDLAS were added to this system (TGA100A
and TGA200A, Campbell Scientific Inc., Logan, Utah, USA; see details
below). Each measured mixing ratios of N2O and CO2. The
analyzers include temperature-controlled, weatherproof cases that allowed
them to be placed on two cinder blocks set on the ground at the base of the
tower (Fig. 1c). Details on the operation of each gas analyzer are given
below. The EC measurements were made at a height of 2.0 m when the surface
was bare; the intakes and sonic anemometer were raised to 1.75 m above the
canopy top throughout the growing seasons. Data from the sonic anemometer
and all three analyzers were recorded at a sampling rate of 10 Hz. Each gas
analyzer included a user-configurable digital filter set for a 5 Hz cutoff
frequency to avoid aliasing.
Sampling system details for each analyzer throughout the
measurement campaign. Changes to equipment during the experimental period
are indicated. Operating configurations of N2O analyzers used in Rannik
et al. (2015) are given for comparison.
Filter
Dryer
Pump
Flow
Cell
Cell
Tube
Tube
pressure
volume
ID
length
(L min-1)
(mb)
(mL)
(mm)
(m)
Current study
TDLAS-LN
Before 8 Jul 2015
2 µmpp
multi-tube
rotary vane
14.5
58
480
4.3
6
After 8 Jul 2015
2 µmpp
multi-tube
rotary vane
14.5
58
4.3
10
TDLAS-TE
Before Jun 2015
2 µmpp
multi-tube
rotary vane
14.5
59
200
4.3
6
After 24 Jun 2015
vortex
single tube
scroll
3.5
32
2.2
9.1
EC155
Before Feb 2016
20 µmss
none
diaphragm
7
950
6
2.7
0.6
After 4 Feb 2016
vortex
6
910
2.2
0.6
From Rannik et al. (2015)
TGA100A (Campbell Scientific Inc.)
17
50
480
4
17.8
CW-TILDAS-CS (Aerodyne Research Inc.)
13.2
53
500
4
16
N2O/CO-23d (Los Gatos Research Inc.)
11.6
117
408*
8
16
QC-TILDAS76-CS (Aerodyne Research Inc.)
13.5
53
500
4
8.5
* From LGR user manual.
Notes: pp is polypropylene; ss is stainless steel.
Gas analyzers
The operating principles of the TDLAS used can be found in Edwards et al. (2003) and Wagner-Riddle et al. (2005). Briefly, a temperature-controlled
laser emits a beam at an absorption wavelength of the gas species of
interest. This beam is split and directed through the sample cell as well as
a reference cell supplied with a known reference gas. The laser wavelength
is swept across an individual absorption line by adjusting the laser
current. This produces the absorption spectrum, measured by detectors at the
ends of the reference and sample cells. The mixing ratio in the sample cell
is calculated from the ratio of the spectral absorbance of the reference and
sample cells. Both TDLASs are capable of simultaneous measurement of multiple
trace gases on separate absorption lines (dual-ramp mode). For periods
during this study, the analyzers were set to measure the absorption of
N2O and 13CO2.
Table 1 lists the equipment and measurement mode used for each analyzer
through the measurement campaign. Operating configurations of N2O
analyzers used in Rannik et al. (2015) are given for comparison. The
analyzers included enclosure temperature controllers to maintain
temperatures of 20 ∘C in winter and 40 ∘C in summer.
Flow from the reference gas cylinders was set at 10 mL min-1. Analyzer
calibrations were performed shortly after the initial installation and
repeated each time a reference cylinder was replaced. Additional information
on calibration and correction of 13CO2 to total CO2 can be
found in the Supplement.
The “legacy” TDLAS (TGA100A, manufactured in 2005, hereafter referred to
as TDLAS-LN) used a cryogenically cooled, lead salt diode laser operating at
83.5 K and 2243.11 cm-1 (N2O) and 2243.585 cm-1 (CO2).
The TDLAS-LN required twice-weekly filling of the laser dewar with liquid
nitrogen. This analyzer was operated in a dual-ramp mode measuring both
N2O and CO2 throughout the measurement campaign, following a
typical air-sampling configuration for EC measurements (Fig. 1d; see also
Mammarella et al., 2010; Mishurov and Kiely, 2010; Molodovskaya, 2012;
Rannik et al., 2015). Air was sampled through a 6 mL rain-shielded intake
(part number 17882, Campbell Scientific, Inc., Logan, Utah, USA) and a dryer
assembly (PD1000, Campbell Scientific, Inc., Logan, Utah, USA), which
included a 2 µm polypropylene filter element in a low-volume,
high-flow filter holder, and a multi-tube dryer (200-tube, 1.2 m
Nafion™ dryer element; Perma Pure, Lakewood, New Jersey, USA). A tee
fitting and needle valves split the flow between dryer purge (3 L min-1) and sample flow directed to the analyzer
(14.5 L min-1; Fig. 1d). Tubing between the intake, dryer assembly, and the analyzer was
4.3 mm ID Synflex™ tubing. The total length of tubing (plus dryer)
from the intake to the analyzer was 6 m from 10 May to 8 July 2015, at
which time the tubing length was increased to 10 m to accommodate the
increase in canopy height. A 950 W rotary vane pump (RB0021, Busch Vacuum
Technics, Inc., Boisbriand, Québec, Canada) was connected to the analyzer
using ∼ 50 m of 25.4 mm ID PVC suction hose (Tigerflex K100,
Kuri Tec, Brantford, Ontario, Canada), to draw air through the analyzer and
the dryer purge (Fig. 1d). The volume of the sample cell of TDLAS-LN was 480 mL. This setup is similar to the TGA100A used by Rannik et al. (2015),
although for the present study the original filter holder (Gelman 1235, Pall
Corp.), included as part of the dryer assembly, was updated to a newer
design (part number 20553, Campbell Scientific, Inc., Logan, Utah, USA) with
lower volume (5 mL compared to 20 mL). Filter elements were replaced
approximately every 2 weeks. The nominal sample-cell pressure was 58 mb
throughout the measurement campaign.
The new N2O analyzer (TGA200A, hereafter referred to as TDLAS-TE) was
similar to the TDLAS-LN with two major upgrades: a smaller-diameter sample
cell and a room-temperature laser. It used a distributed feedback interband
cascade laser (DFB ICL, nanoplus Nanosystems and Technologies GmbH,
Gerbrunn, Germany) that was cooled thermoelectrically as opposed to with
liquid nitrogen. The laser temperature was set to -1 ∘C and the
laser current was tuned to measure N2O at 2237.7 cm-1 and CO2
at 2237.3 cm-1 from 10 to 29 May 2015, when the laser was
configured to measure N2O at 2235.5 cm-1 to assess performance of
the laser in single-ramp mode for approximately 2 months. On 31 July 2015,
the laser was switched back to dual-ramp mode until 31 March 2016. On this
date, a new N2O-only laser was installed in the TDLAS-TE. This laser
operated at -2.4 ∘C at 2208.6 cm-1.
The sample cell of TDLAS-TE has a similar length (1.5 m) compared to the
TDLAS-LN, but a smaller inner diameter (12.7 mm compared to 18.8 mm). The
smaller inner diameter gives the newer sample cell a volume of 200 mL, a
reduction of 280 mL from the original size. From 10 May to 22 June 2015
the TDLAS-TE sampling system (intake assembly, dryer, and pump) was similar
to that of the TDLAS-LN. The TDLAS-TE sampling system was replaced on 24
June 2015 with a new prototype design including a vortex intake and
single-tube dryer (Fig. 1e), optimized to take advantage of the smaller
sample-cell volume. The intake assembly and filter were replaced with a
vortex intake that removes particulates without a traditional filter (US
patent no. 9 217 692). The vortex intake was adapted for this application by
extending the length of the sample tube (2.2 mm ID stainless steel) to 1.1 m and by providing a separate high-capacity filter (Numatics PXB-02, ASCO
Numatics, Florham Park, New Jersey, USA) and diaphragm pump (E163-11-120,
Parker-Hannifin, Inc. Hollis, NH) for the bypass flow. On 24 December 2015
this pump was replaced by one designed for long-term continuous use
(L061B-11, Parker-Hannifin, Inc. Hollis, New Hampshire, USA). The multi-tube
dryer was replaced with a prototype design using a single
Nafion® tube (2.2 mm ID, 7.3 m long TT-110, Perma Pure LLC,
Lakewood, New Jersey, USA). The tube was housed in a large-diameter (25.4 mm) flexible shell to allow it to be purged in reflux mode with little
pressure drop. A needle valve at the outlet of the dryer controlled the
sample-flow rate to 3.5 L min-1. The total length of the intake
assembly and dryer was 9.1 m. A short (0.1 m) tube (4.3 mm ID
Synflex™) connected the needle valve to the analyzer inlet. A
lower-powered (250 W) dry scroll pump (nXDS6i, Edwards Ltd., West Sussex,
UK) pulled the sample air through the intake assembly, dryer,
and analyzer via approximately 50 m of 25.4 mm ID PVC suction hose
(Tigerflex K100, Kuri Tec, Brantford, Ontario, Canada; Fig. 1e). The
nominal sample-cell pressure was 59 mb at the start of the measurement
campaign and set to 35 mb after the new sampling system was installed on 24
June 2015.
CO2 and H2O fluxes were measured with a closed-path EC system
(CPEC200) that included the gas analyzer (EC155) and sonic anemometer (CSAT3A),
as well as the sample pump, enclosures, and mounting hardware. Details on
the EC155 analyzer can be found in Novick et al. (2013). This CPEC200 system
was configured with the manufacturer's valve module for automatic zero and
span, as well as a scrub module to supply dry, CO2-free air for setting
zero on the analyzer (see details in the Supplement). The system
was configured to automatically set the analyzer to zero and CO2 span
daily at 01:00. The EC155 was reconfigured on 10 February 2016 with the
production version of the vortex intake/sample-cell prototype that was
tested by Ma et al. (2017). Nominal flow settings of 8 L min-1 total
flow, with 6 L min-1 through the EC155 sample cell and 2 L min-1
for the vortex bypass, were used.
Frequency-response test
The impulse response method (Sargent, 2012) was used in situ to measure the
frequency response of each EC system and derive the tube delay
(tdel), cutoff frequency (fc), and effective time constant (τe).
Briefly, this method entails injecting an impulse of high N2O
and CO2 concentration into the sample air stream. Measuring the
response to this impulse and taking the Fourier transform of this result
gives the frequency response. This gives a conservative estimate of the
frequency response of the system, as any attenuation of high frequencies
caused by the test method is included in the system frequency response.
The impulse responses were measured in situ for each analyzer by injecting
high-concentration (2500 ppm N2O and 30 000 ppm CO2) gas directly
into the intake with a fast-acting solenoid valve (VHS micro dispense valve,
The Lee Co., Westbrook, Connecticut, USA) driven with a spike-and-hold
driver (part number IECX0501350AA, The Lee Co.) every 10 s, as controlled by
a data logger. For the TDLAS-TE and EC155, which were equipped with the
vortex intake, the dispense valve nozzle was inserted into the rain cap end
for a test of the complete system. The TDLAS-LN used a rain cap that did not
permit this direct injection. This rain cap was removed and replaced with a
small adapter to mount the valve. The dispense valve was driven open for
less than 5 ms for each pulse. For the N2O analyzers, frequency
response was measured at various pressures, including the standard operating
pressure (TDLAS-LN: 29, 38, 45, and 58 mb; TDLAS-TE: 28, 30, 35, and 43 mb).
These pressures were achieved by adjusting the needle valve that sets the
sample flow. The EC155 frequency response was measured with the production
version of the vortex intake assembly. The concentration data and the
voltages to the solenoid valve were recorded at 20 Hz for 20 min per test.
The impulse response was calculated by subtracting the average ambient
background N2O or CO2 concentrations from the data, overlaying the
multiple 10 s periods, and then taking an average of the responses. The
tdel was calculated as the delay from the valve open signal to the peak
of the impulse response, minus processing time for the analyzer (750, 372,
and 800 ms, respectively, for TDLAS-LN, TDLAS-TE, and EC155). This tube
delay represents the physical travel time through the sampling system and
into the analyzer sample cell.
The frequency response was calculated as the normalized amplitude of the
Fourier transform of the impulse response. The fc was determined
graphically as the frequency at which the frequency response reached a value
of 1/√2. Following common practice, we compared this
frequency response to a linear, first-order system (Moore, 1986; Horst,
1997; Massman, 2000; Massman and Lee, 2002; Ibrom et al., 2007). This transfer
function is often related to electronic circuits, but it also describes
transfer function of an ideal mixing volume (Horst, 1997). The amplitude of
the transfer function (TFR) is
TFR=11+ffc2.
Alternately, the transfer function may be parametrized by an effective time
constant (τe), calculated using
τe=12πfc.
Theoretical residence times (τres) of the analyzer sample cells
were calculated based on the sample-cell volume, pressure, and flow rates
from Table 1. Theoretical cutoff frequencies (fc,t) and corresponding
theoretical effective time constants (τe,t) were calculated from
τres using different models for multi-pass and single-pass sample
cells. Multi-pass sample cells, which tend to be relatively short for a
given volume with low length-to-diameter ratios, are often modelled with the
assumption of perfect mixing (τe,t=τres;
Nelson et al., 2004). In contrast, EC155, TDLAS-LN, and TDLAS-TE have
higher length-to-diameter ratios and have relatively long, narrow sample
cells. For these analyzers the theoretical frequency response is modelled
assuming no mixing in the sample cell, which is analogous to line averaging
in a sonic anemometer when the sonic path is aligned with the wind vector.
The amplitude transfer function for line averaging is (Mitsuta, 1966)
TLA=sin2(πτresf)/πτresf2.
This transfer function has an approximate cutoff frequency of
fc,t=2.782πτres.
The corresponding effective time constant from Eq. (2) is
τe,t=12πfc,t=τres2.78.
Flux calculations
Fluxes were calculated as the covariance between fluctuations of the scalar
concentration (s) and the vertical wind velocity (w):
Fs=w′s′‾,
where s is the mixing ratio of either N2O or CO2.
Fluxes were calculated for 30 min time intervals in Matlab™ using
modifications to functions from Sturm et al. (2012). The vertical wind was
despiked and then rotated using a double rotation (Aubinet et al., 2012;
Kaimal and Finnigan, 1994). The high-frequency concentration data for all
analyzers were despiked and filtered according to instrument diagnostic
codes. Concentration measurements from both N2O analyzers were filtered
for periods of equipment maintenance or malfunction. All concentrations
measured by TDLAS-TE and the EC155 were linearly detrended. Concentration
data from TDLAS-LN were linearly detrended until winter of 2016 when the
concentration signal periodically indicated optical fringing of the laser. A
Chebyshev filter was used to detrend and remove the artificial low-frequency
fluctuations for those 30 min periods. The 30 min flux values were filtered
for low wind speed conditions (friction velocity (u∗) < 0.1 m s-1) and wind direction of 60 to 120∘ (shadow of
instrument tower). Vibrations from strong winds against the side of TDLAS-LN
caused increased concentration noise. This occurred during periods when the
field was bare and mean wind speeds were greater than 5 m s-1 from
200 to 360∘. Data from these periods were filtered
out. Flux data from TDLAS-TE were not affected by the strong winds as it was
sheltered by TDLAS-LN for southwest to northerly wind directions, and data
with winds against the side of TDLAS-TE were already filtered out due to
tower interference (Fig. 1).
Cumulative gap-filled seasonal emission rates (planting to harvest) were
calculated for each year. Daily mean emissions were estimated by
extrapolating the mean of the available 30 min fluxes for each day to g N2O–N ha-1. Linear interpolation was used to fill periods with
missing data (Abalos et al., 2015).
Lag times (tl) for analyzers measuring CO2 concentration (TDLAS-TE
when operated under dual-ramp mode; TDLAS-LN and EC155) were calculated
using cross-correlation such that tl was the time delay that maximized
the covariance between the concentration and w signals. CO2 concentrations were used for the tl calculation as the N2O signal
was too weak for cross-correlations for the majority of time periods, as was
also observed by Kroon et al. (2010a). This was determined on a day-by-day
basis as the conditions that affected concentration signal delays (e.g.,
pump performance, filter clogging) remained relatively consistent on a daily
timescale. For the daily lag value, tl was calculated for individual 30 min periods per day with the strongest FCO2
as determined from raw 30 min flux values. An average of six lag times for each analyzer was used for
a given day. Lag times ranged from 0.5 to 1.0 s for the TDLAS-TE and 0.6 to
1.2 s for the TDLAS-LN; tl for the EC155 was consistent at 0.1 s.
Processing delays in the sonic anemometer and the gas analyzers were
subtracted from these lag times to calculate tube delays for comparison with
tdel derived from the frequency-response test. Values for tl derived
from tdel and sonic processing delays were used for periods when the
TDLAS-TE was setup to measure only N2O concentrations.
The τe derived from the frequency-response test was used to
calculate corrections for high-frequency losses using the simplified formula
given by Horst (1997):
Tr=11+2πnmUzτeα,
where U is the mean wind speed, z is the measurement height, and nm and
α are constants determined from the idealized spectra of Kaimal et al. (1972) using values for unstable and stable conditions as presented by
Horst (1997).
Flux comparisons
Half-hourly FN2O and FCO2 obtained with the two TDLAS were compared
over the whole measurement period, and FCO2 from both TDLASs was also
compared to the EC155. Agreement was evaluated based on r2 and slope of
the linear regression between values from the different analyzers.
Cumulative FN2O over the measurement period for each TDLAS were
calculated. No gap filling was applied to the time series, so totals do not
represent absolute emissions for this site. Emissions were accumulated using
only 30 min FN2O when data were available from both analyzers and
converted from ng N2O–N m-2 s-1 to g N2O–N ha-1.
Random errors of each of the individual 30 min FN2O (δF) as
measured by the TDLAS-LN and TDLAS-TE, were calculated using the filtering
method of Salesky et al. (2012). This method was preferred over those
requiring an estimate of the turbulent integral timescale (e.g.,
Finkelstein and Sims, 2001) as timescales can vary depending on definition
(Dias et al., 2004). Salesky et al. (2012) showed that error estimates from
the filtering method compared well to those using integral scales. Briefly,
this method entailed repeatedly applying filters of increasing width
(Δt) to the high-frequency instantaneous fluxes from each 30 min
period. Standard deviations of each set of filtered data were fit to Δt-1/2. These successive power law fits were then fit to the entire
averaging period to estimate the 30 min δF. Propagation of
δF was then used to calculate the confidence intervals (p= 0.05) of the total cumulative FN2O
for the TDLAS-TE and TDLAS-LN time
series.
Flux detection limits (δF) were calculated using the method described
in Blomquist et al. (2010) and Yang et al. (2016) where the detection limit
is calculated using the instrument noise and the variability of the
concentration signal:
δF=2σwTfσCa2τwc+φCn41/2,
such that σw is the standard deviation of w, Tf is the
flux-averaging period, σCa2 is the ambient variance of the
concentration signal, τwc is the integral timescale for the
ambient concentration variance, and ϕCn is the band-limited
analyzer noise. ϕCn was calculated as the mean of the
variance spectra from 1 to 5 Hz. Values of σCa2 were
calculated as the second point of the autocovariance of the concentration
signal (Yang et al., 2016). Blomquist et al. (2010) estimated τwc
using the peak frequency of the variance cospectrum. Peak frequencies here
were modelled via the equation given in Horst (1997).
Daily precipitation and temperature for 2015 and 2016.
Rain values (mm) are the total rainfall per day, and snow values (cm) are the
depth of snow present.
![]()
Results
N<inline-formula><mml:math id="M203" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:math></inline-formula>O fluxes and data coverage
Measurements of fluxes covered two growing seasons (May to October in 2015 and
2016), one post-harvest period (November to December 2015), winter (January
to February 2016), and early spring (March to April 2016). Winter was
classified as the period when nighttime temperatures were consistently below
freezing. Figure 2 displays the mean daily air temperatures and precipitation
for 2015 and 2016. Mean hourly air temperatures ranged from -26.1 to 31.6 ∘C. Winter of 2016 was on average 3 ∘C warmer
than 30-year normals and the maximum snow depth was relatively low at 8 cm.
Precipitation totals were close to 30-year normal values for both years;
however, May of 2015 received only 17 mm of rainfall until 31 May, which was
considerably less than the normal amount of 89 mm. Rainfall was lower than
normal in May, June and July of 2016 with monthly totals 50 % lower than
the 30-year monthly normals.
Non-gap-filled mean daily N2O fluxes obtained with the TDLAS are shown
in Fig. 3. One major N2O flux event occurred in 2015, one day after the
first significant rainfall after planting and fertilizer application (8
and 9 June 2015, day of year (DOY) 159 and 160 in Fig. 3a). The daily
mean 30 min FN2O values for this event as measured by both analyzers
were 600 ng N2O–N m-2 s-1 with the 30 min fluxes during the
daytime and exceeding 800 ng N2O–N m-2 s-1 for several hours.
Two subsequent post-fertilizer flux events occurred after rainfall on 16
and 28 June 2015 (DOY 167 and 179; Fig. 2a) and were smaller in
magnitude with daily average fluxes of 100 ng N2O–N m-2 s-1.
Throughout the remainder of the growing season of 2015 and post-harvest
period of 2015, mean daily fluxes were small and varied between -10 and
10 ng N2O–N m-2 s-1. The warmer-than-average winter in 2016
limited flux emissions from freeze–thaw processes that have been typically
observed at this site (Furon et al., 2008; Risk et al., 2013; Abalos et al.,
2015; Congreves et al., 2017; Wagner-Riddle et al., 2017) with average daily
fluxes ranging from 0 to 30 ng N2O–N m-2 s-1 (Fig. 3b). The
abnormally dry spring in 2016 prevented any significant N2O flux events
associated with fertilizer application. Peak post-fertilizer daily flux
values from 5 May to 13 June 2016 (DOY 126 to 165 in Fig. 3c) averaged
25 ng N2O–N m-2 s-1. Daily flux values in the summer period
of 2016 remained small (∼ 5 ng N2O–N m-2 s-1;
Fig. 3c) but predominantly positive throughout the summer. Mean gap-filled
daily emissions as measured by each analyzer were averaged together. This
gave total seasonal (planting to harvest) cumulative emissions of 3.34 and 1.20 kg N2O–N ha-1 for the growing
seasons of 2015 and 2016, respectively. This is within the range of seasonal
emissions typically observed at this site (Wagner-Riddle et al., 2007;
Abalos et al., 2015) and represented a loss of 2.2 and 0.8 % of the
applied N.
Mean daily fluxes of N2O–N measured by each
analyzer. Panel (a) shows the period of high N2O fluxes in the
post-fertilization period of 2015 and the fluxes measured throughout the
growing season. Panel (b) shows the fluxes measured during the post-harvest
period of 2015 and winter of 2016. The dotted line in panel (b) demarcates the
separation between years 2015 and 2016. Panel (c) shows fluxes measured
from early spring to the end of the growing season of 2016.
![]()
Both analyzers performed well throughout the measurement period and
maintenance requirements were minimal for both. Of the total number of 30 min periods for the whole measurement period, 44 % of the 30 min flux
values were filtered out due to low friction velocity, wind direction and
footprint criteria, warnings from the sonic anemometer from precipitation,
disruption from field operations, and power outages. Further filtering based
on analyzer operation and diagnostics resulted in an additional removal of
16 (TDLAS-TE) and 28 % (TDLAS-LN) of the fluxes due to equipment
failure and periods of equipment reconfiguration. Longer data gaps occurred
mostly for the TDLAS-LN due to pump malfunction, a faulty power supply, and
an electronic module failure. Data outage for the TDLAS-TE in September 2015
was due to failure of the diaphragm pump providing bypass flow for the
vortex intake before a model designed for long-term use was installed.
Intermittent periods of data losses for both TDLASs also occurred when the
enclosure temperature settings prevented the heaters from maintaining a
constant temperature. This occurred at night during spring and fall periods
when differences between daytime and nighttime temperatures were greatest
and midday in the summer months when air temperatures exceeded 30 ∘C.
Analyzer frequency-response characteristics from this
study and that of Rannik et al. (2015). Residence times (τres)
were calculated from values given in Table 1. fc,t is the
theoretical best-case cutoff frequency, assuming no mixing for the
single-pass sample cells and complete mixing for the multi-pass sample
cells, and τe,t is the corresponding effective time constant.
fc and τe are results from the impulse response test. Results
(τe,s and fc,s) from Rannik et al. (2015) were derived from
cospectral analyses. The tube delays tdel are the measured total delay
minus the processing delay. Time constants and tdel are in units
of milliseconds
and cutoff frequencies are in hertz.
τres
fc,t
τe,t
fc
τe
tdel
Current study
TDLAS-LN
114
3.88
41
2.30
69
720
TDLAS-TE
48
9.22
17
–
–
–
TDLAS-TE (vortex)
108
4.10
39
3.50
45
578
EC155
54
8.19
19
4.05
39
100
From Rannik et al. (2015)
fc,s
τe,s
TGA100A (Campbell Scientific Inc.)
100a
4.42
36
1.33
120
–
CW-TILDAS-CS (Aerodyne Research Inc.)
119
1.34
119
2.27
70
–
N2O/CO-23d (Los Gatos Research Inc.)
244
0.65
244
0.61
260
–
QC-TILDAS76-CS (Aerodyne Research Inc.)
116
1.37
116
1.99/0.94b
80/150b
–
a Assumes 15 % of flow is used for purge.
b Calculated with CO2
Frequency-response test
The in situ frequency-response test characterized the frequency response and
tube delay for the three analyzers under the operating pressures tested.
Impulse response peaks were at minimum 5 ppm (N2O) and 600 ppm
(CO2) above ambient concentrations. The peaks were consistent
throughout each of the 20 min tests with an average coefficient of variation
of 1.8 %. The precision of the impulse responses was further improved by
averaging the 120 peaks in each test. Frequency-response curves for each
analyzer obtained at the nominal cell pressure (Table 1) are given in Fig. 4. The frequency-response curve for the EC155 drops steeply at 5 Hz as
expected due to its digital anti-aliasing filter. The effect of the digital
filters is not distinctly visible for the N2O analyzers, but all three
analyzers have little or no response at or near the Nyquist frequency (10 Hz), giving confidence that the frequency-response curves are not
contaminated by aliasing. Table 2 contains the corresponding cutoff
frequencies and effective time constants. The EC155 showed the best
frequency response (fc= 4.05 Hz and τe= 39 ms) for
CO2 (Table 2). The frequency response to N2O of TDLAS-TE was
nearly as good as the EC155 (fc= 3.5 Hz, τe= 45 ms),
while TDLAS-LN showed lower-frequency response (fc= 2.3 Hz and τe= 69 ms).
TDLAS-LN measured both N2O and CO2 during the
frequency-response test; results for CO2 were identical to those for
N2O (data not shown). These measured fc were used in Eq. 1 to
calculate TFR, which are shown for comparison in Fig. 4. Spectral losses
(Eq. 7) calculated with the fc were on average 5 % of the
measured flux for the TDLAS-TE and 7 % for the TDLAS-LN. All measured
fc values were lower than the best-case theoretical cutoff frequencies
(fc,t) calculated from τres (Table 2), indicating some
attenuation of high frequencies in the sampling system components and sample
cells, as expected. Frequency responses for both N2O analyzers were
tested at a range of pressures as varying field conditions can cause
pressures to drift with pump performance and, in the case of TDLAS-LN,
filter clogging. The TDLAS-LN fc increased significantly with pressure
(1.1 Hz at 29 mb to 2.3 Hz at 58 mb), whereas the response of the TDLAS-TE
showed less variation (3.1 Hz at 28 mb to 3.65 Hz at 43 mb; data not
shown). Response times calculated using measured cospectra are included in
the Supplement.
Results of the frequency-response test of TDLAS-LN and
TDLAS-TE for N2O and the EC155 (CO2) under normal operating
conditions (solid lines); dashed lines are modelled TFR calculated using
the measured fc in Eq. (1).
![]()
Tube delays determined from the impulse responses at the nominal cell
pressure for the three analyzers are given in Table 2. As expected, the
EC155 had the shortest tdel at 100 ms. The tube delay of TDLAS-TE was
longer (578 ms) due to the greater tubing length and lower flow rate. The
tube delay for TDLAS-LN was longest at 720 ms, despite its higher flow rate,
due to the larger volumes for its tubing, filter, and dryer. Tube delays for
the N2O analyzers over the range of operating pressures showed a
decrease in tdel with increasing cell pressure (Fig. 5). Decreases in
flow rate with pressure for TDLAS-LN acted to increase tdel to a greater
degree than for the TDLAS-TE. Tube delays determined from the
cross-correlation of CO2 with w ranged from 650 to 1450 ms for TDLAS-LN
and 550 to 1050 ms for TDLAS-TE and were consistent with those determined
from the impulse response (Fig. 5).
Tube delay (tdel) determined from the frequency-response test (solid symbols) and from the cross-correlation between the
CO2 and w (open symbols) across the range of operating pressures. Error
bars are the standard deviation of the mean cell pressure associated with
each value.
![]()
Flux comparison
Comparison of FN2O showed similar values for both TDLASs on short and
long timescales. Figure 6a shows the agreement between 30 min FN2O values
measured by each analyzer (r2 = 0.97, slope = 1.01). The majority
(86 %) of 30 min N2O fluxes were small and below 20 ng N2O–N m-2 s-1. The general correspondence between the TDLAS-LN and
TDLAS-TE flux values remained for the small flux values (Fig. 6b). Scatter
in the low FN2O (Fig. 6b) was related to instrument noise. Both
analyzers showed excellent agreement with the CO2 fluxes measured using
the EC155. The slope and correlation coefficient of the linear regression
between the EC155 and TDLAS-TE were, respectively, 1.03 and 0.998 (Fig. 6c).
Similarly, the slope and correlation coefficient of the linear regression
between the EC155 and TDLAS-LN were 1.03 and 0.998 (data not shown).
Figure 7 shows the mean variance spectra of the N2O signals from each
TDLAS analyzer for a period of 10 days where no N2O fluxes were
observed (29 June to 9 July 2016). The variance spectra of both
analyzers were dominated by instrument noise as N2O emissions were at
background levels. Values of ϕCn were 0.28 ppb2 Hz-1
for the TDLAS-TE and 0.22 ppb2 Hz-1 for the TDLAS-LN. When
considering only instrument noise, detection limits with mean conditions of
U= 3.5 m s-1, z= 4 m, and σw= 0.5 m s-1 were
6.6 ng N2O–N m-2s-1 for the TDLAS-TE and 5.2 ng N2O–N m-2 s-1 for the TDLAS-LN. Incorporating signal noise increased
δFN2O to 9.9 ng N2O–N m-2 s-1 for the
TDLAS-TE and 19.6 ng N2O–N m-2 s-1 for the TDLAS-LN. Despite
the lower instrument noise, concentration signals of the TDLAS-LN were less
steady than the TDLAS-TE during the period evaluated (σCa2of 0.27 and 0.042 ppb2, respectively).
Comparison of 30 min fluxes measured by each analyzer.
Panel (a) displays the comparison of N2O–N fluxes (ng N2O–N m-2s-1) for the entire data set,
panel (b) shows the same N2O–N flux
data set (ng N2O–N m-2s-1) but centred on the low flux
values. The green “x” markers in panel (b) are the fluxes measured
when the standard deviation of background N2O concentrations for
both analyzers was less than 2 ppb N2O. Panel (c) is the comparison of
FCO2 between TDLAS-TE and EC155 (µg CO2 m-2 s-1).
![]()
Emissions were accumulated over the measurement period to evaluate the
long-term comparison of the total FN2O (Fig. 8). No gap filling was
applied for the comparison of cumulated sums between analyzers. From
planting of 2015 to harvest of 2016, TDLAS-TE measured total emissions of
1247 ± 11.3 g N2O–N ha-1 and TDLAS-LN measured 1272 ± 9.3 g N2O–N ha-1, where uncertainties are the propagated random
errors.
Discussion
Both N2O analyzers were well suited for the long-term and continuous
measurement of FN2O that are required to fully capture the temporal
dynamics of N2O emissions (Savage et al., 2014; Shurpali et al., 2016).
Both operated continuously for over 1.5 years through the various
environmental conditions encountered in Southern Ontario. Data gaps were
caused by problems with sample pumps, a power supply failure, and from
reconfigurations for the intercomparison experiment, (e.g., frequency-response testing, swapping lasers, and reconfiguring the sampling systems).
Disregarding these time periods, data coverage of 56 % was typical for EC
measurements at that site after typical EC filtering for rainy periods, low
wind speeds, and wind direction (Santos et al., 2011; Brown et al., 2013).
Further steps can be taken to reduce data losses attributable to the TDLAS
operation, including adjusting the analyzer's internal-temperature set point
more frequently (seasonally to monthly) and protecting the analyzer from
strong winds. A simple baffle the height of the analyzer (0.55 m) along the
side of the analyzer facing the mean wind direction would provide sufficient
shelter from strong winds (U > 5 m s-1).
Mean N2O variance spectra of each TDLAS analyzer
from 29 June to 9 July 2016.
![]()
Cumulative (not gap-filled) N2O–N emissions. Shaded
areas represent the p= 0.05 confidence intervals calculated using the
random error calculations.
![]()
The older TDLAS-LN system required periodic maintenance including liquid
nitrogen fills (twice weekly), replacing filter elements (twice monthly),
and changing pump oil (monthly). The TDLAS-TE system with the vortex intake
and dry scroll pump required no routine maintenance. The TDLAS-TE also
operated using less power for the sample pump than the TDLAS-LN (250 W
versus 950 W). The ruggedness and low maintenance of TDLAS-TE through all
seasons showed the suitability of the analyzer for capturing post-fertilizer
and freeze–thaw emissions.
The TDLAS-TE system, with smaller sample-cell volume, single-tube dryer, and
vortex intake had better frequency response (fc= 3.5 Hz) than
the TDLAS-LN system (fc= 2.30 Hz), while operating at lower
flow rates (3.5 compared to 14.5 L min-1) and with lower sample pump
power consumption. The TDLAS-TE cutoff frequency of 3.5 Hz was only slightly
lower than that of the short-tubed EC155 closed-path analyzer (fc= 4.05 Hz). The frequency response of the TDLAS-TE system was affected
less by changes in pressure and flow rate than the TDLAS-LN, which helped to
maintain constancy of fc with the slight variations in pressure
that occur during long-term field operations. The frequency response of
TDLAS-LN was still adequate for EC measurements (McBean, 1972; Eugster et
al., 2007). Rannik et al. (2015) reported EC system time constants calculated
from cospectra (τe,s) for several N2O analyzers, which
are summarized in Table 2. The same model N2O analyzer (TGA100A) with a
similar sampling system at similar flow (17 L min-1) and pressure
(50 mb) had a τe,s of 120 ms. The lower τe in
this study (69 ms) is likely related to the shorter tube and newer
lower-volume filter holder as compared to the higher-volume filter holder
used in the Rannik et al. (2015) study or discrepancies between our direct
frequency-response assessment compared to deriving τe from
cospectral analyses. Rannik et al. (2015) found a disagreement of almost a
factor of 2 in the N2O and CO2 time constants for a system that
measured both gases, opting to use the value measured for CO2 for
spectral corrections of both gases. However, it is difficult to directly
compare the absolute values of the frequency-response τe with
the commonly used cospectra method (Detto et al., 2011; Peltola et al., 2014;
Rannik et al., 2015; Aubinet et al., 2016). The cospectral method is highly
dependent on the adherence of the spectra to similarity scaling and is also
affected by sensor separation and by imperfect synchronization of the scalar
with vertical wind. The frequency-response test provided a direct assessment
of sampling system response times, as it eliminates the possibility of
response times being affected by artefacts of the temperature or w spectra.
Analyzer sample cell and sampling system design both have an impact on
system frequency response. Multi-pass sample cells tend to be much shorter
but with larger diameters compared to single-pass sample cells; therefore,
the air flow tends to behave as a mixing volume. Measured τe for
analyzers with multi-pass sampling cells are generally greater than the
τres of the sample cell. Nelson et al. (2004) directly measured
the frequency response of an analyzer with a multi-pass sample cell using an
exponential fit to measurements of a step change in concentration. Their
τe of 63 ms was slightly greater than the theoretical time
constant (τres= 54 ms). Neftel et al. (2010) measured the time
constant for an EC system with a similar sample cell using the same
step-change technique. The measured τe of 400 ms was more than
twice the theoretical time constant (τres= 175 ms) of the
sample cell, likely due to the long tube (30 m) and other sampling
components. Single-pass sample cells, which tend to be long and narrow, have
less mixing as the air flows through the cell, giving them a theoretical
advantage of a factor of 2.78 compared to multi-pass sample cells. The
TDLAS-TE τe was within 15 % of this theoretical τe,t, indicating near ideal performance in the sample cell and sampling
system. The EC155 τe was a factor of 2 larger than the
theoretical τe,t. This is likely due to mixing in the sample
cell, which has a length / diameter ratio of 15, compared to 110 for the
TDLAS-TE. TDLAS-LN has a diameter / length ratio of 75, suggesting its
performance should also approach the ideal. However, the TDLAS-LN τe was 40 % greater than τe,t, compared to 15 % for
TDLAS-TE. This additional frequency-response degradation was most likely
caused by attenuation in the multi-tube dryer. Ratios of τe / τe,t from similar analyzers operating without driers (Table 2) were
comparable to those of the TDLAS-LN and TDLAS-TE systems, although not
directly as spectral calculations of τe were used in that study.
Operating the N2O systems with driers prevented uncertainties from
spectroscopic and density corrections. Improper application of the
WPL (Webb–Pearman–Leuning)
corrections, as well as variations in flux processing steps, can affect flux
values (Aubinet et al., 2012; Mammarella et al., 2016). For fluxes close to
zero, WPL corrections for fluctuations in water vapour can be greater than
the value of the scalar flux, and uncertainty of the correction can be of
similar magnitude to the small fluxes (Detto et al., 2011). Line broadening
caused by moisture in the sample can also cause significant errors in
FN2O (Neftel et al., 2010).
Lag times can be difficult to determine using cross-correlation methods on
data with weak signals, which is often the case for N2O (Pihlatie et
al., 2005; Neftel et al., 2010). Inaccurate lag times can lead to
underestimations of flux values. The tdel from the impulse response tests
corresponded well with the tl obtained for CO2 using the
cross-correlation method. The tdel measured with the impulse response
method reduced uncertainty in the N2O lag times used in the flux
processing in the absence of a CO2 signal. This approach is useful for
long-term studies measuring agricultural N2O fluxes when signals are
too weak for automatic covariance maximization and when variations in
operating conditions preclude the use of a constant lag value. Tube delay of
the TDLAS-TE (0.6 s at 35 mb) was lower than that of the TDLAS-LN (0.8 s at
58 mb), showing the improved transit time of the upgraded intake system.
These values were comparable to tube delay values of analyzers operated
without driers (Rannik et al., 2015), showing how the TDLAS systems used
here (with driers) could be optimized for fast response, while also avoiding
corrections associated with lack of air drying.