Open-path Fourier transform infrared spectroscopy
(OP-FTIR) has often been used to measure hazardous or trace gases from hot
point sources (e.g. volcano, industrial, or agricultural facilities) but
seldom used to measure greenhouse gases (GHGs) from field-scale sources
(e.g. agricultural soils). Closed-path mid-IR laser-based N2O,
nondispersive-IR CO2 analysers, and OP-FTIR were used to measure
concentrations of N2O and CO2 at a maize cropping system during
9–19 June 2014. To measure N2O and CO2 concentrations accurately, we
developed a quantitative method of N2O/CO2 analysis that minimized
interferences from diurnal changes of humidity and temperature. Two
chemometric multivariate models, classical least squares (CLS) and partial
least squares (PLS), were developed. This study evaluated various methods to
generate the single-beam background spectra and different spectral regions
for determining N2O and CO2 concentrations from OP-FTIR spectra. A
standard extractive method was used to measure the actual path-averaged
concentrations along an OP-FTIR optical path in situ, as a benchmark to
assess the feasibilities of these quantitative methods. Within an absolute
humidity range of 5000–20 000 ppmv and a temperature range of 10–35 ∘C, we found that the CLS model underestimated N2O
concentrations (bias =-4.9±3.1 %) calculated from OP-FTIR
spectra, and the PLS model improved the accuracy of calculated N2O
concentrations (bias =1.4±2.3 %). The bias of calculated
CO2 concentrations was -1.0±2.8 % using the CLS model. These
methods suggested that environmental variables potentially lead to biases in
N2O and CO2 estimations from OP-FTIR spectra and may help OP-FTIR
users avoid dependency on extractive methods of calibrations.
Introduction
Agriculture contributes a substantial amount of greenhouse gas (GHG)
emissions (e.g. N2O, CO2, and CH4) to the global GHG budget
(IPCC, 2007; Cole et al., 1997; Smith et al., 2008). Among these gases,
N2O is mainly emitted from agricultural soils, accounting for 38 % of
the global anthropogenic non-CO2 GHG emissions from agricultural
activities (US-EPA, 2006; Smith et al., 2007). Nitrous oxide is produced
from biological reactions that transform available N in soils via microbial
nitrification and denitrification (Mosier et al., 2004). Considering that
the global warming potential value of N2O is 310, it is estimated that
overall GHG emission from soils (based on CO2 equivalents) is
approximately 2500 Mt of CO2 equivalent per year. A significant fraction of soil
N2O emissions results from the use of nitrogen (N) fertilizers in
agricultural soils. In addition to contributing to the overall GHG burden of
the atmosphere, N2O emissions also represent a direct loss of N applied
to the field, contributing to the decreased nitrogen use efficiency (NUE)
(Eichner, 1990; Ryden and Lund, 1980; Bremner et al., 1981; Omonode et al.,
2017). Also, soils play the role of a sink or a source for atmospheric
CO2 (Paustian et al., 1997; Smith et al., 2008). Changing land use of
crop production, especially agriculture-related uses such as tile drainage
and tillage management, and agricultural lime application (e,g., CaCO3
and MgCa(CO3)2) potentially become a large source of CO2
released to the atmosphere via microbial decomposition of soil organic
carbon (Smith, 2004; IPCC, 2007; Cole et al., 1997; West et al., 2005).
The flux chamber measurement has been the most common method to measure GHG
emissions from soils (Denmead, 2008; Rochette and Eriksen-Hamel, 2008).
Chamber measurements, however, are subject to significant limitations that
lead to uncertainties and biases in estimated GHG emissions. For instance,
because chambers have a small footprint (∼0.5 m2) and
generally wide sampling intervals (usually once to twice a week), they are
poorly suited for evaluating spatial and temporal variabilities of GHGs from
agricultural soils (Laville et al., 1999; Rowlings et al., 2012; Schelde et
al., 2012). Also, wind turbulence is known to substantially induce more gas
transportation from soils to the atmosphere. Chamber methods do not take
this wind-induced effect into account, and this likely results in
underestimations of gas emissions (Denmead and Reicosky, 2003; Poulsen et
al., 2017; Pourbakhtiar et al., 2017). It is worth mentioning that the eddy
covariance flux measurement method, one of the most common
micro-meteorological techniques used to investigate gas exchanges in the
agroecosystem, is capable of measuring gas fluxes frequently with an
increased footprint (Baldocchi, 2003). A large-scale flux measurement
(hundred metres to several kilometres) using this method, however, makes
comparisons among field-scale treatments (1–5 ha) more difficult than
chamber methods (Schmid, 1994; Denmead, 2008).
Open-path Fourier transform infrared spectroscopy (OP-FTIR) is a
non-intrusive sensing approach and capable of detecting multiple components
simultaneously, acquiring real-time data at a relatively high temporal
resolution (seconds to minutes) and providing path-averaged gas
concentrations (Russwurm and Childers, 1996). OP-FTIR has been applied to
measuring atmospheric gases since the 1970s (e.g. hazardous air pollutants;
fugitive volatile organic compounds, VOCs; and trace gases) (Herget and
Brasher, 1980; Gosz et al., 1988; Russwurm et al., 1991; Bacsik et al.,
2006; Briz et al., 2007; Lin et al., 2008). More recently, OP-FTIR has been
increasingly used to measure GHGs or other trace gases in agriculture,
mostly in animal facilities (e.g. N2O, CO2, CH4, and
NH3 from swine or dairy production) (Childers et al., 2001a; Loh et
al., 2008; Bjorneberg et al., 2009; Barrancos et al., 2013; Naylor et al.,
2016). Only a few studies, however, implemented OP-FTIR to measure gas
emissions from vegetable production fields or fertilized soils (Bai et al.,
2014, 2018; Ni et al., 2015). Integrating OP-FTIR with
micrometeorological techniques (e.g. flux gradient or backward Lagrangian
stochastic dispersion methods) can measure gas fluxes from the field-scale
source of interest with high temporal and spatial representations that are
less prone to artefacts induced by point-based sampling (Flesch et al., 2004, 2016; Bai et al., 2014, 2018; Ni et al., 2015). Moreover, the OP-FTIR
combined with a scanning system can potentially be applied to horizontally
or vertically survey numerous fields of interest and measure their gas
emissions simultaneously (Flesch et al., 2016).
Despite these advantages, OP-FTIR also faces a number of challenges. In
order to resolve the spectral features of GHGs, high spectral resolution
(< 0.5 cm-1) is required to resolve the rotation–vibrational
absorption bands of the GHGs of interest (Griffiths and de Haseth, 2007).
Calculating concentrations from FTIR spectra requires both a sample single-beam spectrum and a reference/background spectrum that does not contain
spectral contributions from GHGs of interest, which is not possible at the
field scale (e.g. evacuation of the field); thus, mathematical methods have
been developed which strip the spectral bands from a sample single-beam
spectrum. This challenge requires the use of instrumental- or
spectral-processing methods to create a background spectrum, and these
methods are subject to biases in determining GHG concentrations (Griffiths
and de Haseth, 2007; Russwurm and Childers, 1996). Furthermore, the
atmosphere contains a high concentration of water vapour that interferes
with the detection and quantification of GHGs of interest (Russwurm and
Childers, 1996; Horrocks et al., 2001; Briz et al., 2007; Smith et al.,
2011). These challenges of data processing and the interferences from water
vapour likely introduce biases and uncertainties in GHG quantification.
Using error-prone concentrations in flux prediction models
(micrometeorological techniques) possibly leads to unknown uncertainties in
estimated gas fluxes. Thus, it is essential to develop a comprehensive
quantitative method to improve and assure the quality of gas quantification
using OP-FTIR.
Testing the feasibility of quantitative methods and qualities (accuracy and
precision) of OP-FTIR is challenging because a reliable reference is
required to validate FTIR-derived concentrations. One of the most common
approaches was to position a gas cell filled with known gas concentrations
of interest in the optical path and test gas quantitative methods
(Russwurm et al., 1991; Horrocks et al., 2001; Smith et al., 2011). This
approach, however, somewhat controlled the environment and neglected the
effect of ambient interferences, such as water vapour, on the quality of gas
quantification. The alternative approach is to compare the derived
concentrations with ambient concentrations. The ambient concentration of a
gas of interest can be determined by averaging the global background
concentrations (e.g. N2O∼ 310 ppbv or
CO2∼ 400 ppmv) or measured from the gas samples that were
collected along the OP-FTIR path and analysing their concentrations using
laboratory-based gas chromatography (GC) (ASTM, 2013; Childers et al., 1995;
Kelliher et al., 2002; Bai et al., 2014). The experimental designs of these
assessment approaches, either the point sampling setup or low sampling
frequency or both, became the major problem for cross validating their
OP-FTIR quantitative methods. Since the ambient concentrations likely
fluctuate from place to place (e.g. different land uses) and at different
times (e.g. diurnal or seasonal variation), the spatial and temporal
variations of the ambient concentration were potentially misconceived as
bias in gas quantification. Up to now, only three studies continuously
measured real-time ambient concentration to logically cross validate
quantitative methods and data qualities under fluctuating environmental
factors (e.g. the dynamic water vapour), but none of the prior studies
actually assessed their methodologies for quantifying N2O
concentrations (Briz et al., 2007; Reiche et al., 2014; Frey et al., 2015).
Therefore, the objectives of this study were to (1) develop a long-path gas sampling system that can continuously collect numerous gas samples
simultaneously along an optical path of OP-FTIR and measure path-averaged
concentrations to evaluate quantitative qualities of N2O and CO2 concentrations derived from OP-FTIR spectra; and (2) optimize the
quantitative method, including post-data processing, analytical window
selections, and chemometric multivariate algorithms, that is less sensitive
to interferences of ambient humidity and temperature and capable of
determining N2O and CO2 concentrations accurately.
Materials and experimental methodsSite description
This study was conducted at the Purdue University Agronomy Center for
Research and Education near West Lafayette, Indiana, in the United States
(86∘56′ W, 40∘49′ N;
elevation 215 m). The experimental site was located between two fields
(∼3.5 ha per field) with a continuous corn system since 2013.
Gas measurements began just after an anhydrous ammonia application with
total N rate of 220 kg NH3-N ha-1 on 9 June and ended on 19 June 2014. The soils were classified as Drummer silty clay loam (fine-silty,
mixed, mesic Typic Endoaquoll) with a bulk density of 1.6 g m-3, organic matter of 3.4 %, soil pH of 6.0, and cation exchange
capacity of 23 cmolc kg-1 (0–20 cm). During 9–19 June,
the cumulative precipitation was 57 mm, and the average soil temperature and
moisture from the depth of 0–10 cm were 23±3∘C and
0.32±0.06 cm3 cm-3, respectively, which were
determined by the on-site weather station.
Instrumentation setup
The spectrometer was a monostatic open-path FTIR air monitoring system
(model 2501-C, MIDAC Corporation, Irvine, CA). This instrument included the
IR source, interferometer, transmitting/receiving telescope, mercury cadmium
telluride (MCT) detector, and ZnSe optics. A mid-IR beam in the spectrometer
passed through the atmosphere along an optical path and returned to the
telescope after reflection from a retroreflector to collect spectra that
included information about the gas of interest. A cube-corner retroreflector
with 26 cubes was mounted on a retractable tripod with 150 m physical path
length from the telescope, corresponding to an optical path length of 300 m
(Fig. 1).
Schematic of the instrumentation used to assess the accuracy of
N2O and CO2 concentration determined by OP-FTIR in this study. DFG
N2O and LI-840 CO2 analysers combined with the synthetic open-path
air-sampling system (S-OPS) were used to measure the actual path-averaged
N2O/CO2 concentrations and benchmark the N2O and CO2
concentrations calculated from OP-FTIR spectral analyses. The humidity, air
temperature, and wind information were measured from the weather station.
Ambient concentrations of N2O and CO2 were also determined
independently to assess the bias and precision. A difference frequency
generation (DFG) mid-IR laser-based N2O gas analyser (IRIS 4600,
Thermo Fisher Scientific Inc., Waltham, MA) and the non-dispersive infrared
(NDIR) spectrometer CO2 gas analyser (LI-840, LI-COR Inc.,
Lincoln, NE) were used to measure N2O and CO2 concentrations of
the sampled gases from a synthetic open-path gas sampling system (S-OPS)
(Fig. 1). The DFG laser-based N2O analyser determined N2O
concentrations in the mid-infrared wavelength with a high precision of
< 0.15 ppbv (1σ, 3 min averaging). An NDIR CO2 analyser
exhibited high accuracy (< 1.5 % of reading) and low noise
(< 1.0 ppmv) to determine CO2 concentrations using a single-path, dual-wavelength, and infrared detection system.
A 50 m long S-OPS combined with a gas sampling system (GSS) was used to
collect gas samples along an optical path of OP-FTIR. An S-OPS consisted of
9.5 mm diameter Teflon® tubes and 10 inlets fitted with 1.0 µm
Teflon® filters. The inlet flow rates were adjusted by critical
orifices to 0.70 L min-1 (±10 %). Gas samples were
drawn through an S-OPS line by a sampling pump in the GSS at approximately 7 L min-1 and collected into a Teflon® ambient pressure
chamber. Then, N2O and CO2 analysers drew air samples from the
ambient pressure chamber to measure the actual path-averaged
concentrations of N2O and CO2 along the OP-FTIR path (Heber et
al., 2006). The measured N2O and CO2 concentrations were used to
benchmark concentrations calculated from the OP-FTIR spectrum. Temperature,
relative humidity, and pressure in the ambient pressure chamber were also
recorded every 30 s to monitor the performance of the GSS.
Meteorological measurements of air temperature and relative humidity were
measured using an HMP45C probe (Vaisala Oyj, Helsinki, Finland) at 1.5 m
above ground level (m a.g.l.). The meteorological data were collected by a
data logger (model CR1000, Campbell Scientific, Logan, Utah) and averaged
every 30 min. Wind speed and direction were acquired from a 3-D sonic
anemometer (model 81000, RM Young Inc., Traverse City, MI) mounted at 2.5 m
height on the meteorological mast and recorded at 16 Hz. The recorded data
were telemetered to the on-site instrumentation trailer.
Overview of ambient temperature, water vapour content, and concentrations of
N2O and CO2
The 30 min averages of ambient N2O and CO2 concentrations were
determined by the S-OPS, and water vapour content and air temperature were
measured at the meteorological station (Fig. 2). During the test, 793 valid
OP-FTIR spectra with known concentrations of N2O, CO2, water
vapour, and air temperature were collected. A total of 90 spectra containing
338±0.3 ppbv N2O and 93 spectra containing 400±3.0 ppmv CO2 were selected from these valid spectra to calculate
concentrations of N2O and CO2, respectively, using different
quantitative methods. These groups of spectra with consistent
N2O/CO2 concentrations but covered by broad ranges of water vapour
content and air temperature were used to examine the effect of water vapour
and temperature on concentration calculations.
The 30 min averaged concentrations of (a)N2O and (b)CO2 were measured using N2O and CO2 analysers by sampling the
air from S-OPS, and the 30 min averages of (c) water vapour content and (d) air temperature were also measured from the on-site weather station during
9–19 June 2014. The concentrations of N2O, CO2, and water vapour
shown in these figures were measured while the air was well-mixed (U > 1.5 m s-1). The light grey bars mean the
OP-FTIR spectra contained 338±0.3 ppbv N2O and the dark grey
bars mean the OP-FTIR spectra contained 400±3.0 ppmv CO2. Both
selected spectra (N2O 338 ppbv, n=90; CO2 400 ppmv, n=93)
covered the broad ranges of water vapour and air temperature and were used
to assess the sensitivity of the OP-FTIR quantitative methods to dynamic
ambient variables.
OP-FTIR data acquisition and QA/QC procedure
A spectra range of 500.0–4000.0 cm-1 and a resolution of 0.5 cm-1
were selected for spectra acquisition. Each sampled spectrum was acquired by
co-adding 64 single-sided interferograms (IFGs) using the AutoQuant Pro4.0
software package (MIDAC Corporation, Irvine, CA). The IFGs were converted to
single-beam (SB) spectra using a zero-filling factor of 1, triangular
apodization, and Mertz phase correction. A stray-light SB spectrum was also
acquired by daily pointing the transmitting/receiving telescope away from
the retroreflector at the beginning of the experiment using the same
parameters (Russwurm and Childers, 1996). Each sampled SB spectrum was
stray-light corrected by subtracting the stray-light SB spectrum from the
sampled SB spectrum before converting to the absorbance spectrum.
The IFGs and corresponding SB spectra were influenced by ambient factors
that included wind-derived vibrations, scintillation induced by air mixing,
water vapour content, dust accumulation, and condensation on the
retroreflector. Criteria of quality assurance were based on the inspection
of the IFG and SB spectra, following the standard guideline in the MIDAC
instrumentation manual and the FTIR open-path monitoring guidance documents
(Russwurm and Childers, 1996) with the supplement criteria published by
Childers et al. (2001b) and Shao et al. (2007) to acquire high-quality
spectra. The maximum and minimum of the IFG centre bursts were controlled
between approximately 0.61 and 1.14 V of the analogue-to-digital converter based on the physical path length of
150 m. Any IFG centre-burst signals > 2.25 V were rejected to
avoid a non-linear response of the MCT detector.
Spectral analysesAn absorbance spectrum converted from a single-beam (SB) spectrum
To calculate a concentration for a given solute, a stray-light corrected SB
spectrum is ratioed against an SB background spectrum (GHG-free) to produce
an absorbance spectrum from which the gas concentration is determined using
the Beer–Lambert law. As discussed earlier, OP-FTIR measurements do not
permit the collection of a background spectrum that is free of GHGs. Two
different approaches were used in this study to overcome this constraint.
Both methods required a normal SB spectrum corresponding to the path
length of interest that was then mathematically manipulated to produce a
background spectrum. A representative field SB spectrum and the regions of
interest for each GHG are shown in Fig. 3a. For the zapped background
method, a background (zap-bkg) was obtained by drawing a straight line
between two selected points which removed, or “zapped”, any spectral
contributions below the line using OMNIC Macro Basic 8.0 commercial software
(Thermo Fisher Scientific, Inc.). This is illustrated for the N2O
region of interest in Fig. 3b, with the two points and the line labelled
as zapped background. For the zap-bkg method, one quality SB spectrum was
selected to create a zap-bkg each day, and all of the sampled SB spectra
collected from one day were converted to absorbance spectra using this
zap-bkg. Another method, referred to as the synthetic background method,
was generated from this same original SB spectrum using IMACC software
(Industrial Monitoring and Control Corp., Round Rock, TX). In this case,
numerous points in the non-absorbing region of the SB spectrum were
selected as base points, and a high-order fitting function was used to
construct a background spectrum. An example in the N2O/CO2 regions
is illustrated in Fig. 3b and labelled synthetic background (syn-bkg).
Six points within 2050.0–2500.0 cm-1 were selected to fit the curvature
of the SB spectrum using a polynomial function to create a syn-bkg SB
spectrum (Fig. 3b). The mathematically manipulated SB spectra were used as
background files to convert the sampled SB spectra into absorbance spectra
(Fig. 3c and d). For the syn-bkg method, all data points were stored as one
data file, and this file was applied to each sampled SB spectrum to create
its syn-bkg. Since the selected points determined the curvature of the
syn-bkg SB spectrum, it is critical to choose the data points that do not
introduce any distortion (e.g. artificial dips and peaks) into the syn-bkg.
In general, we avoided selecting data points within the absorption feature
of interest (e.g. 2170.0–2224.0 cm-1 for N2O analysis), and the
number of data points used to fit the curvature of the SB spectrum was
considered adequate if it produced a smooth function (Russwurm and Childers,
1996). Adding too many data points may lead to artificial distortion in a
syn-bkg. Because the syn-bkg is one of the recommended methods for spectral
analysis (ASTM, 2013), it was used to assess the feasibility of the zap-bkg
method.
The illustrations of (a) a field single-beam (SB) OP-FTIR
spectrum containing the regions of N2O, CO2, and water vapour
collected through an optical path length of 300 m; (b) zapped and synthetic
SB backgrounds (zap-bkg and syn-bkg) generated from this field SB spectrum
and used to convert the sampled SB spectrum to (c) the absorbance spectra
that allowed the calculation of N2O/CO2 concentrations using the
Beer–Lambert law.
Gas quantifications: multivariate models and spectral window
selections
Based on the Beer–Lambert law, we used reference spectra to predict gas
concentrations from field absorbance spectra. In this study, classical least
squares (CLS) and partial least squares (PLS) regressions were used to
calculate N2O and CO2 concentrations. The details of these two
methods are described as follows.
CLS prediction model. Each of the reference spectra used in the CLS model
contained only one gas component (e.g. N2O, CO2, or water vapour),
and these reference spectra were generated from the
high-resolution transmission molecular absorption (HITRAN)
database (Rothman et al., 2005). The CLS model (AutoQuant Pro4.0) predicted
gas concentrations from the field absorbance spectra converted using the
zap-bkg method. In addition, CLS spectra were also calculated using the
IMACC software to predict gas concentrations from the spectra converted by
the syn-bkg method. The non-linear function between the actual and predicted
gas concentrations of the reference spectra was selected in the CLS model in
both quantitative packages.
PLS prediction model. Each of the reference spectra used in the PLS model
consisted of multiple gas components (e.g. an N2O plus H2O mixing
spectrum). Gas samples were delivered to a multi-pass gas cell (White cell)
with an optical path length of 33 m (model MARS-8L/40L, Gemini Scientific
Instruments, CA). Spectra were collected by a laboratory-based FTIR
spectrometer (Nexus 670, Thermo Electron Corporation, Madison, WI), which
included a globar IR source, a KBr beam splitter, and a mercury cadmium
telluride high-D* (MCT-High D*) detector. The FTIR spectrometer was purged
with dry air (-20∘C dew point) produced by a zero air generator
(model 701H, Teledyne, Thousand Oaks, CA). Certified N2O was diluted
with ultra-pure N2 gas using a diluter (series 4040, Environics Inc,
Tolland, CT), and the water vapour content was controlled by a Nafion tube
(Perma Pure, Lakewood, NJ) contained within a sealed container of saturated
water vapour. Temperature and humidity were monitored using a humidity and
temperature transmitter (model HMT330, Vaisala Oyj, Helsinki, Finland). The
N2O concentrations were diluted from 30 ppmv to 0.30, 0.40, 0.50, 0.60,
and 0.70 ppmv and mixed with water vapour to a relative humidity of 20 %, 40 %,
60 %, and 80 % at 303 K. Spectra were acquired at 0.5 cm-1 resolution
and averaged from 64 sample scans with triangular apodization. A total of 60 spectra of N2O plus H2O mixtures were used to build the PLS model using
quantitative spectral-processing software (Thermo Fisher Scientific TQ
Analyst version 8.0). In order to avoid overfitting the models, the optimum
set of factors used in PLS models was determined by cross validation and
justified by the prediction of residual error sum of squares (PRESS)
function. The correlation between known and PLS-predicted concentrations was
used to quantify N2O from the field absorbance spectrum converted by
syn-bkg within given spectral windows.
Spectral window selections. The window selection (Fig. 4) was critical
because of interferences of water vapour. While a broader window contained
more information of the gas of interest and potentially improved the
spectral fit between the modelled and sampled spectra and the quantitative
accuracy, it also included more features of water vapour and led to biases
in gas quantifications. On the other hand, a narrow window can minimize the
interfering effect of the uninteresting gases but may reduce the spectral
information of the targeted gas, which lead to biases in gas calculations
(e.g. underestimation of gas quantification). The window used for N2O
quantifications was 2130.0 to 2224.0 cm-1, which mainly includes the
absorbance features of N2O (P branch) and water vapour, and other
regions (WN1–4 shown in Fig. 4a) were also selected for calculating
N2O concentrations. For CO2, the spectral windows of 2070.0–2085.0 and 722.0–800.0 cm-1 (not shown) contain features of CO2
and water vapour (Rothman et al., 2005). Multiple windows (WC1–3 shown
in Fig. 4c) were selected to calculate CO2 concentrations and assess
the effect of water vapour on gas predictions.
Field and HITRAN reference absorbance spectra: (a) field
spectrum containing the features of N2O and water vapour, (b) reference
spectra of N2O and water vapour at 2170.0–2224.0 cm-1, (c) field spectrum containing the features of CO2 and water vapour, and (d) reference spectra of CO2 and water vapour at 2070.0–2084.0 cm-1. WN(1–4) and WC(1–3) denote the spectral windows used to
calculate N2O and CO2 concentrations from field spectra.
The accuracy of the FTIR-calculated concentration and statistical
analysis
Bias, the relative error between the S-OPS and OP-FTIR-measured
N2O/CO2, indicated the accuracy of the calculated N2O or CO2 concentrations using different spectral analyses (i.e. background
types, multivariate models, and spectral windows) and can be calculated with
Eq. (1):
Bias=xi-xtxt×100%,
where xi is the N2O or CO2 concentration calculated from
the OP-FTIR spectrum, and xt is the known N2O or CO2
concentration measured by the S-OPS. The calculated biases were
statistically analysed by ANOVA procedures and the protected least significant
difference (LSD) was used for multiple comparisons among population mean
biases (α=0.05) (SAS 9.3; SAS Institute Inc., 2012).
Results and discussionQuantitative methods (SB backgrounds, spectral windows, and multivariate
models)
Both SB background methods (zap- and syn-bkg) were used to convert the
sampled SB spectra to absorbance spectra for gas quantifications. Different
windows (WN1–4 for N2O and WC1–3 for CO2) were used to
calculate N2O/CO2 concentrations from absorbance spectra using CLS
and PLS models. A series of the OP-FTIR spectra acquired from broad ranges
of humidity (i.e. 5000–20 000 ppmv water vapour) and temperature (10–35 ∘C) were used to calculate N2O and CO2 concentrations.
Within these ranges, the mean bias (%) indicated the accuracy of N2O
or CO2 quantification, and the standard deviation (SD) referred to the
sensitivity of quantitative methods to water vapour content and air
temperature.
Nitrous oxide (338 ppbv)
Spectral windows experiencing less water vapour interference generally improved the accuracy of N2O quantification. In the
CLS model, N2O concentrations calculated from the absorbance spectra
converted by zap-bkg were underestimated by 10.7±2.3 % using the
broadest window (WN1: 2170.0–2223.7 cm-1 shown in Fig. 4a). This
bias was reduced using WN2 (2188.5–2223.7 cm-1) (i.e. bias =-9.1±2.5 % shown in Fig. 5a). Likewise, N2O concentrations
derived from the absorbance spectra converted by syn-bkg were underestimated
by 8.2±2.6 % using the WN1. This bias was reduced using
WN3 (2215.8–2223.7 + 2188.5–2204.1 cm-1) (i.e. bias =-5.6±2.6 % shown in Fig. 5b). Although interferences of water
vapour can be mitigated by narrowing down spectral windows, the narrowest
window (WN4: 2188.5–2204.1 cm-1) used in the CLS model resulted in
greater biases than the WN3 in both zap- and syn-bkg procedures (Fig. 5a and b). The narrowed window also lost N2O absorption features and
presumably increased biases if the analytical window was over-confined. The
P-branch feature of N2O extended from 2130.0 to 2223.7 cm-1, and
this region was also used to calculate N2O concentrations. In the CLS
model, the window of 2130.0–2223.7 cm-1 showed the minimum mean bias of
-0.4 % of the calculated N2O concentrations using syn-bkg (data not
shown); however, this window was sensitive to interfering water vapour and
led to the highest variability in N2O estimations (i.e. -0.4±5.3 %).
The box plots of the calculated N2O concentrations
and the corresponding biases from a series of OP-FTIR spectra (n=90) that
contain 338±0.3 ppbv N2O with varying humidity and air
temperature using different SB background-processing methods (zap-bkg and
syn-bkg) and four spectral windows (WN1–4) in the CLS and PLS models:
(a) zap-bkg + CLS model, (b) syn-bkg + CLS model, and (c) syn-bkg + PLS model. The plot displays the mean (□), median (–),
interquartile ranges (box), and extreme values (whiskers). Different letters
indicate significant differences (p < 0.05) among the means
calculated by different quantitative methods by the least significant
difference (LSD).
As previously mentioned, it was important to generate a reasonable
background for the spectral analysis. In the CLS model, the bias of N2O
quantification using the syn-bkg was significantly lower than the zap-bkg
based on the same spectral window (WN1–3; p < 0.05) (Fig. 5a
and b). The syn-bkg method coupled with the integrated window of
2215.8–2223.7 and 2188.7–2204.1 cm-1 (WN3) was
considered the optimal combination for N2O quantifications using CLS
models (i.e. lowest bias =-5.6±2.6 % in CLS shown in Fig. 5b).
This optimal combination was also used in the PLS model to predict N2O
concentrations. The mean bias of the calculated N2O was reduced from
-5.6 % (CLS model) to -0.3 % (PLS model) (Fig. 5b and c). As compared
with the CLS model, the PLS model significantly improved the accuracy of
N2O quantification (p < 0.05) presumably because the PLS
algorithm can extract useful latent factors from the N2O plus H2O
mixing spectra (e.g. the contribution of water vapour to N2O).
Carbon dioxide (400 ppmv)
For CO2 estimations, three spectral windows were used in the
2070.0–2084.0 cm-1 range (Fig. 4c). The accuracy of CO2
quantification was also improved by narrowing down spectral windows (Fig. 6). In the CLS model, CO2 concentrations calculated from the absorbance
spectra converted by zap-bkg were underestimated by 6.4±4.1 %
using the broadest window (WC1: 2070.0–2084.0 cm-1). This bias was
reduced by the narrowed window of WC2 (2075.5–2084.0 cm-1) (i.e.
bias =-0.1±4.2 % shown in Fig. 6a). The bias of the calculated
CO2 concentrations was -4.7±2.5 % using WC1 coupled with
syn-bkg and reduced to -0.3±2.4 % using WC2 (Fig. 6b). The
most confined window (WC3: 2075.5–2080.5 cm-1) resulted in greater
biases than WC2, and particularly in conjunction with zap-bkg (i.e.
bias =3.2±3.4 % shown in Fig. 6a). Thus, the range from 2075.5
to 2084.0 cm-1 (WC2) was the optimal window for CO2
quantification using the CLS model (Fig. 4c).
The box plots of the calculated CO2 concentrations
and the corresponding biases from a series of OP-FTIR spectra (n=93) that
contain 400±3.0 ppmv CO2 with varying humidity and air
temperature using different SB background-processing methods (zap-bkg and
syn-bkg) and three spectral windows (WC1–3) in the CLS model: (a) zap-bkg and (b) syn-bkg. The plot displays the mean (□), median
(–), interquartile ranges (box), and extreme values (whiskers). Different
letters indicate significant differences (p < 0.05) among the means
calculated by different quantitative methods by the least significant
difference (LSD).
The zap-bkg led to a greater underestimate in N2O (bias =-10±2.3 % shown in Fig. 5a) than CO2 calculations (bias =-0.1±4.2 % shown in Fig. 6a) based on the optimal window (WN3 and
WC2) used in CLS models. Since the absorbance feature of CO2 at
2076.9 cm-1 (the band centre) was less complicated than the P branch of
N2O from 2170.0 to 2223.7 cm-1, the CO2 absorbance converted
by zap-bkg was similar to syn-bkg (Fig. 3c and d). Therefore, the
calculated bias showed that there was no significant difference between zap-
and syn-bkg methods for CO2 concentration calculations using the
WC2 (Fig. 6). Zap-bkg, however, led to the higher variability in the
calculated CO2, indicating that simply removing the CO2 feature by
the linear function potentially resulted in biases for CO2
quantification.
The other potential region for CO2 quantification was within
722.0–800.0 cm-1 (the R branch of CO2ν2 band shown in
Fig. 3a). Different windows were examined for calculating CO2
concentrations using the CLS model in this region, and the CO2
concentrations were underestimated by 40 %–70 % no matter which window was
used in conjunction with zap-bkg. The mean bias was minimized (bias =-9.0±2.9 %) by using two windows of 723.0–727.7 and
732.0–738.5 cm-1 in conjunction with syn-bkg (data not shown). As
compared with the results from the 2070.0–2084.0 cm-1 range (Fig. 4c),
the 722.0–800.0 cm-1 window resulted in a significant underestimation
of CO2 concentration because (1) more water vapour features interfered
with the R branch of CO2 features in the 722.0–800.0 cm-1 range
than CO2 in the 2070.0–2084.0 cm-1 range and (2) it was difficult
to simulate the appropriate background at the low wavenumber region in the
SB spectrum.
Diurnal N2O and CO2 estimations
The quantitative approach leading to the minimum bias in N2O
estimations was to use syn-bkg with the WN3 window in the PLS model
(Fig. 5c). For CO2, only the CLS model was used for calculating
concentrations because of missing CO2/H2O mixing spectra for PLS
models. The approach leading to the minimum bias in CO2 estimations in
this study was to use syn-bkg with the WC2 window in the CLS model
(Fig. 6b). These procedures were used to estimate N2O and CO2
concentrations from the OP-FTIR spectra collected from 9 to 19 June 2014
(Fig. 7). The diurnal fluctuations in N2O and CO2 concentrations
corresponded to diurnal changes of wind speed and air temperature. The
higher N2O/CO2 concentrations were usually measured during the
night because of N2O/CO2 accumulations. The accumulation of
N2O/CO2 occurred near the ground when turbulent mixing was low,
resulting from decreasing buoyancy from the ground surface (i.e. a stable
atmosphere). The greater density of air parcels due to decreasing
temperature also led to gas accumulation. The diurnal variation in CO2
was greater than N2O (Fig. 7b), and we hypothesized it was due to
multiple sources of CO2. While N2O was mostly produced from soils
via microbial nitrification and denitrification, CO2 was emitted via
soil respiration (including microbes and corn root) as well as respiration
from grass and corn leaves.
Measurements of air temperature, wind speed, N2O, and
CO2 concentrations from 9 to 19 June 2014. The 30 min averages of (a) air
temperature and wind speed; (b)N2O concentrations measured from S-OPS
using the DFG N2O analyser and calculated from OP-FTIR using the method
of (syn-bkg +WN3 + PLS), as well as the corresponding biases; and (c)CO2 concentrations measured from S-OPS using LI-840 CO2 analyser
and calculated from OP-FTIR using the method of (syn-bkg +WC2 + CLS), as well as the corresponding biases.
Mixing of the surface layer of air tended to result in greater homogeneity
along the optical path. Under low wind speed, the presumably poorly mixed
air increased the variability of the path-averaged N2O/CO2
concentrations along the optical path, resulting in the difference between
the 50 m S-OPS and the 150 m OP-FTIR. The calculated biases of N2O and
CO2 were 1.3±2.6 % (n=363) and -0.7±5.8 %
(n=327), respectively, while the mean wind velocity ranged from 0.1 to 8.4 m s-1 (Fig. 7). The variability of the calculated biases of
N2O and CO2 was reduced when the data that were collected in low
wind speeds (< 1.7 m s-1) were excluded, i.e.
biasN2O=1.4±2.3 % (n=298) and biasCO2=-1.0±2.8 % (n=272).
Conclusions
We have developed and evaluated different methods for quantifying
concentrations of nitrous oxide and carbon dioxide using open-path FTIR
based on combinations of single-beam backgrounds (zap-bkg and syn-bkg),
analytical windows (WN1–4 and WC1–3), and chemometric multivariate
calibration models (CLS and PLS). It is challenging to generate the P-branch
N2O absorbance within 2170.0–2223.7 cm-1 to predict N2O
accurately but feasible to generate absorbance within 2075.5–2084.0 cm-1 for CO2 prediction using the zap-bkg method. The principle
for selecting spectral windows is that using the region with less water
vapour features while over-confining the analytical region may lead to
biases in gas predictions. The CLS model, the most common approach used for
gas retrievals in OP-FTIR commercial packages, underestimates N2O
concentrations but predicts CO2 accurately within an absolute humidity
range of 5000–20 000 ppmv and a temperature range of 10–35 ∘C.
In this study, the method resulting in the minimum bias for N2O
quantification is to use the combination of syn-bkg, a two-band window
(2188.7–2204.1 + 2215.8–2223.7 cm-1), and the PLS model (N2O
bias =1.4±2.3 %). The method leading to the minimum bias in
CO2 quantification is to use the combination of syn-bkg, the
2075.5–2084.0 cm-1 window, and the CLS model (CO2 bias =-1.0±2.8 %). We describe comprehensive methods of
N2O and CO2 analyses for the increasing number of OP-FTIR users who
are interested in greenhouse gas emissions from agricultural fields.
Data availability
All measurement data used in this study are archived at the Purdue
University Research Repository (PURR) under r
10.4231/06W5-J904 (Lin et al., 2019).
The supplement related to this article is available online at: https://doi.org/10.5194/amt-12-3403-2019-supplement.
Author contributions
CHL, CTJ, RHG, and AJH designed the lab- and field-FTIR measurements experiment and spectral analyses. CHL and RHG conducted the FTIR experiment. CHL conducted the data analysis and prepared the manuscript with contributions from CTJ, RHG, and AJH.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
The authors would like to thank Tony Vyn and Terry West for crop and field management, Austin Pearson for data collection and analysis, and the Purdue University Climate Change Research Center for the additional travel grant support.
Financial support
This research has been supported by the United States Department of Agriculture National Institute for Food and Agriculture, USDA NIFA (grant no. 13-68002-20421), and the Indiana Corn Marketing Council (grant no. 12076053).
Review statement
This paper was edited by Frank Hase and reviewed by three anonymous referees.
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