Dynamic chamber method
An improved method for measuring soil N2O fluxes using a
quantum cascade laser connected to a dynamic chamber
N. J. Cowan a, b, D. Famulari a, P. E. Levy a, M. Anderson a, M. J. Bell c, R. M. Rees c, D. S. Reay b, U.
M. Skiba b
Centre of Ecology and Hydrology, Edinburgh, b Edinburgh University, Edinburgh, c Scotland’s Rural University
College, Edinburgh
a
Nicholas Cowan (nicwan11@ceh.ac.uk)
Summary:
Using an Aerodyne Research Inc. compact quantum cascade laser (QCL), a dynamic chamber method was
developed and was used to measure fluxes of N2O from soils at higher precision and temporal resolution than
previous experiments have allowed. The dynamic method was compared to the more commonly used static
chamber method. Results suggest that the dynamic method is capable of measuring soil N2O fluxes with a
minimum uncertainty of approximately 1 - 2 µg N2O-N m-2 h-1. The continuous record of N2O and CO2
concentration at 1 Hz during chamber closure provides an insight into the effects that incubation time and the use
of either linear or non-linear regression methods have when employed with static chamber systems similar in
design. Results suggest that reported fluxes of N2O measured using static chamber methods which use linear
regression can be approximately 25 % smaller than those calculated using a dynamic chamber.
Introduction
Nitrous oxide (N2O) is a potent greenhouse gas (GHG) and the single largest contributor to global stratospheric
ozone depletion (Ravishankara et al., 2009). The majority of N2O is released into the atmosphere by the natural
microbial processes of nitrification and denitrification (Davidson et al., 2000), but human activities (such as the
wide scale use of nitrogen fertilisers) have resulted in a significant increase in global N 2O emissions since preindustrial times (IPCC 2007). Global N2O fluxes have large uncertainty associated with them (55 – 75 %) (IPCC
2007) because of the large temporal and spatial variability of N2O fluxes, and the uncertainty inherent in the
methodology predominantly used to measure them (Folorunso & Rolston, 1985; Velthof et al., 1996).
1
Dynamic chamber method
Almost all measurements use the closed, non-steady-state (or “static”) chamber method (Hutchinson &
Mosier, 1981), due to its simplicity and low cost (de Kleine & Harvey, 2013). In this method, gas samples are
extracted from a chamber sealed on the soil surface during a 30 – 60 minute incubation period, and later analysed
using a gas chromatograph (GC) instrument. The flux is inferred from the rate of change in gas concentration
within the chamber. Because of the constraints imposed by the logistics of extracting samples and subsequent
laboratory analysis, the sample size is typically limited to 2-4 samples per chamber closure. Consequently the
fluxes calculated by any regression model are poorly constrained (Pedersen et al., 2010). Furthermore, data can
be noisy, and it is not always clear which regression model is the most appropriate for fitting to the data (Levy et
al., 2011). The resolution of GC instruments tends to be rather low (> 10 ppb for N2O), meaning that small
fluxes may not be clearly detectable.
Previous attempts to improve the precision of N2O flux measurements, using infrared spectroscopy to
measure concentration changes of N2O within chambers, were limited by the low resolution of the instruments
available (Yamulki & Jarvis, 1999; Laville et al., 2011), the logistical constraints of operation, and cost (Hensen
et al., 2006; Hensen et al., 2013). However, advances in infrared laser technology have recently produced fastresponse (> 10 Hz) N2O analysers with high sensitivity (< 5 ppb), capable of operating in the field (Laville et al.,
1999; Jones et al., 2011). In this study, we used a commercially-available infrared continuous wave quantum
cascade laser (QCL) with a resolution of 30 ppt. Pulsed QCL instruments (resolution of 1.5 ppb) have been used
successfully to measure N2O fluxes using the eddy covariance method (Eugster et al., 2007; Kroon et al., 2007;
Kort et al., 2011). Here, we incorporated this instrument into a dynamic non-steady-state chamber design, which
allows for significantly improved accuracy and precision when measuring N 2O fluxes. This paper describes the
system design, the analysis of the high-resolution data obtained, and comparison with conventional static
chamber measurements.
Materials and methods
We constructed a non-steady-state flow-through (or closed loop dynamic) chamber system (Livingston &
Hutchinson, 1995; Hensen et al., 2006) hereafter referred to as the dynamic chamber method, in which a closed
volume of air was circulated between a chamber and the QCL gas analyser via a pump (Figure 1 & 2). A compact
continuous wave quantum cascade laser (CW-QC-TILDAS-76-CS, Aerodyne Research Inc., Billerica, MA,
USA) was used to measure gas concentrations within the chamber. This instrument uses tuneable infrared
2
Dynamic chamber method
differential absorption spectroscopy (Zahniser et al., 2009), with a 0.5 litre multi-pass absorption cell, with an
optical path length of 76 metres. The laser source requires a very stable temperature to operate, and a solid-state
cooling system (Thermocube, SS cooling systems, NY, USA) kept the system at a constant temperature of 10 ˚C
by pumping a cooled solution of water and ethanol (20 %). The cell was kept at a constant low pressure
(approximately 45 Torr) using a dry-scroll vacuum pump (SH-110, Varian Inc, CA, USA). The inlet of the QCL
was fitted with a manual ball valve and a needle valve to control the air flow rate and cell pressure, as well as a
safety valve attached to the pump to prevent back-flow. A 0.45 micron particle filter was attached to the inlet of
the absorption cell.
The chamber consisted of a cylindrical polyvinyl chloride (PVC) plastic pipe of 48 cm inner diameter
(ID) and 22 cm height. The chamber had PVC flanges fitted at the top and bottom. A 3 mm thick square
aluminium metal lid was fitted to the top of the tube and sealed using epoxy resin and silica sealant. A 1 cm layer
of closed cell neoprene sponge was fitted around the bottom flange. The chamber was placed onto a collar which
could be inserted several cm into the soil (on average 5 cm). The collar consisted of a PVC flange attached to a
stainless steel ring (2 mm thickness, 6 cm height). The closed cell neoprene sponge attached to the underside of
the chamber formed an airtight seal with the collar.
The chamber was fitted with an internal fan (3000 rpm, Delta Electronics Inc., Taiwan) and air
temperature probe (CS109, Campbell Scientific, UT, USA). A pressure sensor (CS100, Campbell Scientific, UT,
USA) fitted externally to the lid of the chamber measured the internal air pressure. All fittings on the lid of the
chamber were sealed with silica sealant to avoid gas leakage. The temperature and pressure sensors were
connected to a data logger (CR1000, Campbell Scientific, UT, USA) which stored data every second. The
chamber was fitted with a rubber flap (1 mm thickness, 6 cm width) which could be rolled down to shield the seal
formed between the neoprene sponge and the base of the collar or chamber from wind.
Two 30 m lengths of 3/8 inch ID Tygon® tubing were attached to both the inlet of the QCL and the
outlet of the pump. This provided a 30 m radius from the QCL in which the chamber could be placed. Tygon ®
tubing was used as it allowed flexibility in the movement of the chamber and does not interact with N 2O. A flow
rate of approximately 6 to 7 L min-1 was used between the QCL and the chamber. There was a lag time of
approximately 20 seconds between gas leaving the chamber and entering the analyser.
Prior to each
measurement, the chamber was purged with ambient air for one minute, and the data for the first minute
following closure was discarded. The chamber volume was estimated after each measurement by recording depth
from the chamber top to the soil surface at ten points.
3
Dynamic chamber method
To investigate the effect that circulating air through the chamber had on internal pressure, we made
measurements in the lab with the chamber sealed on an impermeable metal base. Care was taken to ensure that
the chamber was completely sealed using strong clamps and bolts to seal between the neoprene layer and the
metal base. In these measurements, the chamber was fitted with a very high sensitivity differential pressure
sensor (PX654, Omega Engineering Inc., CT, US) with a precision of 0.1 Pa. A flow rate of 6 l min -1 reduced
pressure in the chamber by approximately 3 Pa (due to the drop in static pressure with fluid speed). This drop in
pressure was considered to be considered negligible when compared to the natural variation in air pressure caused
by wind and temperature variation (> 100 Pa).
Field measurements were made at two locations. Firstly, we measured on grass turf at our institute (near
Penicuik, Scotland, 55° 51' 42.827"N, 3° 12' 21.6393"W), where we enhanced N2O fluxes by applying 50 g of
ammonium nitrate dissolved in 5 litres of water and spread over 10 m2 (17.5 kg N ha-1). Four collar rings were
inserted into the treated area and one collar ring was inserted into an untreated area as a control. Measurements
were made from each of the 5 chambers on 4 separate days, each lasting approximately one hour. Secondly, we
made measurements in a grazed field at Crichton near Dumfries, S-W Scotland (55° 2' 31.3238"N, 3° 35'
16.4393"W) where different fertiliser types (synthetic urine, cattle urine, cattle urine and dicyandiamide (DCD)
and cattle dung) had been applied by SRUC at rates of 425, 435, 425 and 720 kg N ha-1 respectively.
Fluxes of N2O were calculated using linear and non-linear asymptotic regression methods (Equations 1 4) using the HMR package for the statistical software R (Pedersen et al., 2010). The regression method that
provided the best fit for the time series of concentration was chosen for each individual measurement, using
goodness-of-fit statistics and visual inspection.
1.
Linear regression: Fluxes are calculated using the standard line of best fit through the data:
𝐶𝑡 = 𝑎 + 𝑏 × 𝑡
(Eq.1)
Where Ct is the gas concentration at time t, and dC/dt is:
𝑑𝐶
𝑑𝑡0
= 𝑏
(Eq.2)
4
Dynamic chamber method
2.
HM model This is a commonly used non-linear model derived by Hutchinson and Mosier (1981) with a
negative exponential form of curvature. The change in C with t is given by:
𝐶𝑡 = 𝐶𝑚𝑎𝑥 − (𝐶𝑚𝑎𝑥 − 𝐶0 ) exp(−𝑘𝑡)
(Eq.3)
Where C0 is the initial concentration, Cmax is the value at equilibrium and k is a constant, and calculates
dC/dt0 as:
𝑑𝐶
𝑑𝑡0
= 𝑘(𝐶𝑚𝑎𝑥 − 𝐶0 )
(Eq.4)
Once the rate of change in concentration of a particular gas is known it can then be used to calculate soil
flux for each measurement (See Equation 5). The flux can then be converted to the appropriate units by simple
unit conversion factors.
𝐹=
𝑑𝐶
𝑑𝑡0
·
𝜌𝑉
𝐴
(Eq.5)
Where F is gas flux from the soil (nmol m−2 s−1), dC/dt0 is the initial rate of change in concentration with time in
nmol mol−1s-1, ρ is the density of air in mol m-3, V is the volume of the chamber in m3 and A is the ground area
enclosed by the chamber in m2.
Static chamber measurements were made at Crichton using identical chambers, following an existing
protocol (see www.GHGPlatform.org.uk). Chambers were sealed for 40 minutes, then a single sample taken via a
three-way tap in the lid. All gas samples were collected via a 20 ml syringe and stored in evacuated 20 ml glass
vials. Rather than sampling gas concentration at the time of closure, this was estimated from 10 samples of
ambient air collected during the measurement day. All of the samples were analysed on a 7890A GC System
fitted with an ECD and FID detector (Agilent Technologies, CA, USA) at SRUC. The concentration change
inside the static chambers was calculated by subtracting the concentration of N2O measured within the chamber
(at t = 40 minutes) from the daily average ambient N2O concentration. Using Eq.5, the flux of N2O from each
5
Dynamic chamber method
chamber was calculated assuming that the concentration change within the chamber was linear. The static and
dynamic chamber methods were compared by measuring on the same collars on the same day or within 24 hours.
Results and discussion
Chamber Design
Concentration change in the dynamic chamber showed a very consistent pattern, and appeared close to linear over
short periods of 180 – 300 seconds (Figure 3). We rarely saw effects of disturbance caused by placing the
chamber onto a collar. Emissions of CO2 from soils were generally larger and more consistent than N2O.
Abnormal behaviour in the CO2 concentration during the enclosure periods was a useful way to identify leaks or
instrument problems. However, in initial tests, strong gusts of wind (> 10 mph) did have a clear influence on the
observed concentrations within the chamber, by inducing an air excursion between the neoprene gasket and the
collar (Figure 4). To counter this, a ring of rubber made from a bicycle inner-tube was used to form a skirt which
could be rolled to cover the seal between the chamber and the collar. Subsequent to fitting this skirt, no further
effects of wind on the concentrations within the chamber were observed, and the concentration pattern was as
shown in Figure 3.
During the testing phase of this dynamic chamber we observed that significant leaks from chambers can
be caused by wind. Wind effects were only observable from the high frequency concentration measurements by
the QCL and are not commonly reported for static chambers, where concentration measurements are made at
much lower time frequency (> 600 seconds). The leakiness during windy conditions will vary according to
chamber design and wind speed. If chambers are not able to prevent wind from causing leaks the fluxes measured
may be reduced significantly as ambient air enters the system. This effect would also vary temporally due to the
variation in wind speed which could make it an even larger source of uncertainty over long measurement
campaigns. It is our recommendation that chambers used to measure fluxes from exposed locations are rigorously
tested and wind proofed before being used in the field.
6
Dynamic chamber method
The influence of length of chamber enclosure on flux
In order to investigate the effect of enclosure time on the calculated flux, the dynamic chamber was closed for
one hour. Fluxes were calculated for time periods ranging from 180 to 3600 seconds (Figure 5). Fluxes
calculated over the first 180 – 300 seconds were consistently larger than those made over longer time periods.
This was still the case when dC/dt appeared to be linear, and when the HMR model was fitted, which should in
principle account for a non-linear rate of increase. The difference in the flux after one hour using linear and nonlinear regression can be very significant (over 300 % of reported flux in some cases) which suggests that
regression choice is a very large source of error over long time periods. On average, the flux calculated over 1
hour (3600 seconds) was 75% of that calculated over 180 seconds when using linear regression, and 85 % using
HMR (Figure 6). The large variation between the behaviour of the concentration change in each individual
measurement resulted in large uncertainty values in the results (Figure 6); however a clear negative correlation
with time can be observed for both regression methods.
Regression fitting and method used to calculate fluxes from static chamber measurements is believed to
be one of the largest sources of errors in flux calculations (differences greater than 70% between linear and nonlinear methods) (Kroon et al., 2008; Pedersen et al., 2010; Parkin et al., 2012). This topic is highly debated
within the flux chamber community. A large analysis of flux data from a network of sites across the UK
concluded that in over 90% of cases, the fluxes of N 2O followed a linear accumulation, however, where
accumulations were non-linear the flux could be underestimated by 26% (Chadwick et al., 2013). All of these
observations will depend on chamber design, soil properties and measurement strategies for which there is no
universal solution.
Increasing the number of concentration methods over an hour from 4 to 3600 increases the ability of the
dynamic chamber to accurately calculate the best regression model to use for individual chamber measurements.
The results from the one hour long measurements in this paper agree that choice of regression would most likely
be a large source of uncertainty when taking a small number of concentration measurements over a long period
of time (10 to 60 minutes). When using the dynamic chamber over a 180 – 300 second period in this study there
was usually very little deviation from a linear fit; however this cannot be said for measurements which last for
over 600 seconds. The linearity observed in each measurement is different, although the reasons for the
differences in these measurements are unclear. Leakage, soil diffusion, chamber microclimate and biological
activity, or a mixture of each, are all possible theories that may explain this effect.
7
Dynamic chamber method
Spatial variability is considered by many in the N 2O flux community to be the largest source of error
when scaling up N2O flux measurements to incorporate larger areas such as national or global inventories.
(Folorunso & Rolston, 1985; Velthof et al., 1996). The importance of improving our understanding of the
regression errors in chamber measurements is that it is a large and possibly systematically biased source of error
which is fixable. Results in this paper suggest that using linear regression over 1 hour periods may significantly
bias flux measurements towards lower flux values. If this is the case, then increasing the number of chamber
measurements to increase the accuracy of flux estimates may have limitations. Many measurements will not
prevent a bias towards lower fluxes as additional measurements will also be affected by the same bias.
Comparison of dynamic and static chamber methods
Comparison of static and dynamic chamber approaches at identical locations showed a systematic
underestimation of 29 % difference in flux estimated from static chamber measurements compared with the
dynamic QCL chamber fluxes with a correlation coefficient of 0.71 (Figure 7). Given that the static chambers
were run over 40 minutes (2400 seconds) and assumed to be linear, we would expect them to be approximately
20 % lower than the dynamic chamber fluxes calculated over 180 seconds, based on Figure 6. These results
agree in terms of a bias towards a lower flux reported using the static chamber method when compared to the
dynamic chamber method. The lack of agreement between individual measurements using both methods will
partly be because of the large uncertainty in the static chamber method, but also because the time difference
between measurements on the same chambers by the different methods was as much as 24 hours in some cases.
Background fluxes measured in the non-fertilised control plots of the field experiment using the static
chamber method ranged between -32 to 44 µg N2O-N m-2 h-1. By comparison, fluxes measured from the same
locations using the dynamic QCL method were consistently below 10 µg N2O-N m-2 h-1, with calculated
uncertainty of approximately 1 µg N2O-N m-2 h-1. The mean flux values calculated from all control plot
measurements using the dynamic and static chamber methods are 2.5 and 5.4 µg N2O-N m-2 h-1 respectively
using linear regression. Both methods report similar mean flux values for the control plots; however the range of
flux measurements from the control plots varies significantly between the methods (Figure 8). The low range of
fluxes measured using the dynamic chamber suggests that the method is able to provide measurements with a
higher consistency and precision of low N2O flux from soils than the static chamber is able to.
8
Dynamic chamber method
The comparison of methods shows that the dynamic chamber is able to measure a wide range of fluxes
to within a close range of the static chamber method. The high correlation between the fluxes measured using
both methods is encouraging. The estimate made using Figure 6 to predict the relationship between the flux and
enclosure time ( 20% decrease over 40 minutes) is fairly accurate when comparing the flux measured from the
dynamic and static chambers (29 % decrease). This may indicate that the static chamber tendency towards lower
fluxes is due to the use of linear regression over 40 minutes. The results obtained from the control plots show the
precision of the dynamic chamber method when measuring low fluxes. The mean flux values reported for the
control plots do not differ largely. This suggests that by making many static chamber measurements it is possible
to provide a fairly accurate representation of low flux behaviour from soils.
Uncertainty calculation
For each measurement, we estimated the uncertainty associated with each of the terms in Eq. 5, and propagated
these to estimate the total uncertainty in the flux. Uncertainty in dC/dt was obtained from the 95% confidence
interval in the regression slope parameter. Uncertainty in the volume error could be estimated by taking several
measurements of height in each chamber, and taking the 95% confidence interval in the calculated volume. This
gave values of approximately 10 % of the total.
Uncertainty in the air density term, rho, arises from
uncertainties in the temperature and pressure measurements. The 95% confidence interval for the mean
temperature and pressure was calculated from the 1 Hz data, and added to the instrumental precision of the
temperature probe (0.4 °C) and pressure sensor (50 Pa). For the static chambers measurements, it was not
possible to calculate regression uncertainty as concentrations during chamber closure were only measured twice,
immediately when the chamber was sealed and 40 minutes later. A previous study has estimated that the realistic
confidence intervals based on uncertainty arising from instrumental and regression models are typically of an
order of 20% that of the measured flux, although this can vary significantly (Levy et al., 2011).
The results of the one hour long and comparison measurements suggest that the uncertainty in dC/dt can
be large (minimum of approximately 20 µg N2O-N m-2 h-1) using the static chamber method, but is reduced to
typically < 10 µg N2O-N m-2 h-1 in the dynamic QCL chamber method. Uncertainties from the temperature and
pressure measurements are small and should apply to both methods (ranging from 0 to 2.83 µg N 2O-N m-2 h-1).
In the dynamic QCL chamber method, only the volume term remains as a significant source of error; this is
9
Dynamic chamber method
because errors in volume scale linearly with flux. Only occasionally does the uncertainty in dC/dt contribute
significantly, where there is not a good fit with concentration measurements (Figure 9).
The dynamic chamber used with the QCL provides significantly more data than the static chamber
method from which uncertainties from individual chamber measurements can be confidently estimated. This
allows a detailed investigation of how to improve flux measurements as well as provide a clearer picture of the
true spatial variability of N2O fluxes from soils. The largest source of error in static chambers comes from
estimating dC/dt (Levy et al., 2011). This uncertainty is not as large using the dynamic chamber method. The
largest source of error which could be eliminated from the dynamic chamber method is that of volume. The
volume uncertainty can be very difficult to address on non-uniform soils. The addition of vegetation can
complicate things further. Further work looking at ways to reduce volume estimation error in chambers would be
the next step in increasing the accuracy of the dynamic chamber method.
Advantages and disadvantages
The dynamic chamber method is adaptable and able to work with a variety of instruments and chamber
designs. A significant advantage of this particular setup is that the high precision laser instrument can be used for
both micrometeorological measurements and chambers alternately without a lot of effort. The biggest advantage
of linking a high precision laser to a dynamic chamber over conventional static chambers is the high resolution
of N2O concentration measurements. The development of these methods is vital to improving the accuracy of
GHG measurements which can then provide reliable information on the efficacy of mitigation of N2O from a
variety of agricultural sources.
Currently the biggest setbacks of using the dynamic system are the initial setup cost and the power
requirements of the system. The mobility of the instrumentation is limited as a constant power supply is required.
This may be provided by a mains electricity supply or a mobile generator. Large batteries would provide a better
alternative to generators, although the cost of the system would rise significantly. To avoid long lag times we
limited the tubing to 30 metre, which unfortunately limited the distance accessible for measurements on soft
surfaces without the use of rugged off-road vehicles. There are also difficulties in using the dynamic chamber
methods to make large a large number of simultaneous measurements often demanded by field experiments
comparing different treatments.
10
Dynamic chamber method
Conclusions
Using precise rapid gas analysers such as a QCL in combination with chambers provides more reliable data than
the conventional static chamber/GC approach. Physical and statistical uncertainties and errors can be calculated
with confidence even when fluxes are very small (below 10 µg N2O-N m-2 h-1). Closed loop dynamic chamber
and static chamber methods can deliver roughly the same number of flux measurements (10) within one hour,
assuming 4 samples are withdrawn from static chambers within this one hour. Currently high costs, power
consumption, weight and poor transportability limits the use of fast dynamic chamber approaches to measure
N2O or CH4 fluxes. It will only be a matter of time that the point source flux measurements, currently delivered
for N2O and CH4 by static chambers, will be of same quality as available for soil respiration measurements. It is
important that errors and uncertainties in these systems are understood before dynamic chamber methodologies
become widely used. The high precision, ability to measure several gases including isotopic variances will
advance our understanding of soil processes and associated emissions of N2O and CH4.
Acknowledgements
We thank SRUC, especially A. Bagnall for providing us with the chamber and flux data from the Crichton field
site. We also thank DEFRA for financial support through the GHG platform project inveN2Ory.
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Dynamic chamber method
Figure 1 Airflow between gas analyser and chamber in a dynamic closed loop system (Adapted from Hensen et
al., 2006)
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Dynamic chamber method
Figure 2 Schematic diagram of the dynamic closed loop chamber
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Dynamic chamber method
Figure 3 Example of N2O and CO2 concentration measurements recorded at 1 Hz by a QCL during 3 chamber
measurements using the dynamic chamber from 3 different locations within close range. Fluxes are visualised by
viewing the change in concentration over time. Each measurement lasts approximately 180 seconds of which the
first 60 seconds are discarded from the regression analysis. (SRUC plots, Crichton, November 2012)
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Dynamic chamber method
Figure 4 Example of N2O measurements affected by wind over a 30 minute period without the use of the wind
blocking skirt. Concentration changes within the chamber should look close to linear (as in Figure 3). The
dilution effect is more obvious when higher gas concentrations are present within the chamber. (Measurement
made on mowed grass with added ammonium nitrate, CEH Edinburgh, March 2012)
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Dynamic chamber method
Figure 5 Four examples of one-hour long chamber measurements of N2O concentration (black dots joined)
plotted with the corresponding flux values calculated at calculated at different time intervals using linear (white
diamonds) and non-linear (HMR) (black squares) regression methods. Fluxes were calculated at t = 180, 300,
600, 900, 1800, 2700 and 3600 seconds after chamber enclosure. The initial 60 seconds of concentration
measurements are removed for the regression analysis. (Measurement made on mowed grass with added
ammonium nitrate, CEH Edinburgh, March 2012)
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Dynamic chamber method
Figure 6 Mean values of flux calculated from all hour long chamber measurements expressed as a fraction of the
initial flux calculated at 180 seconds. Fluxes were compared at t = 180, 300, 600, 900, 1800, 2700 and 3600
seconds after chamber enclosure. Error bars represent the 95% confidence interval of the mean values.
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Dynamic chamber method
Figure 7 A direct comparison of N2O flux measurements made using the dynamic and static chamber methods at
a SRUC field site in Dumfries (October to November 2012) remove CLHAN. The dotted line represents a 1:1
ratio of comparison. The dashed line is the line of best fit through all data points.
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Dynamic chamber method
Figure 8 A comparison of N2O fluxes measured from untreated control plots using both dynamic and static
chamber methods. The box plot represents the 25th and 75th percentile ranges. The ability of the dynamic
chamber method to measure background flux precisely is highlighted by the low range of uncertainty.
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Dynamic chamber method
Figure 9 A representation of all of the calculated uncertainties made using the dynamic chamber method at the
Crichton field site. The estimated uncertainty in dC/dt in static chambers (20%) is added as a comparison with
literature estimates (Levy et al., 2011). The results highlight the improved ability to measure dC/dt precisely
using the dynamic chamber method.
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