N2O exchange over managed grassland: Application of a quantum cascade laser spectrometer for micrometeorological flux measurements

Agricultural and Forest Meteorology 150 (2010) 775–785

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N2O exchange over managed grassland: Application of a quantum cascade laser spectrometer for micrometeorological flux measurements§ Albrecht Neftel a,*, Christof Ammann a, Cornelia Fischer a, Christoph Spirig a, Franz Conen b, Lukas Emmenegger c, Bela Tuzson c, Susanne Wahlen c a

Agrosocope Reckenholz-Ta¨nikon Research Station ART, Reckenholzstrasse 191, 8046 Zu¨rich, Switzerland Institute of Environmental Geosciences, University of Basel, Bernoullistrasse 30, 4056 Basel, Switzerland c Swiss Federal Laboratories for Materials Testing and Research, Ueberlandstrasse 129, 8600 Du¨bendorf, Switzerland b

A R T I C L E I N F O

A B S T R A C T

Article history: Received 12 December 2008 Received in revised form 22 July 2009 Accepted 23 July 2009

A novel quantum cascade laser absorption spectrometer (QCL-AS) was tested to monitor N2O exchange fluxes over an intensively managed grassland using the eddy covariance approach. The instrument employs a continuous wave quantum cascade laser to scan over the absorption features of N2O, CH4 and water vapor at 7.8 mm. The precision of the N2O flux measurements was determined to be 0.2 nmol m2 s1 but the accuracy can easily be affected by water vapor interferences twice as large. These water vapor interferences are not only due to the respective gas dilution effect but also due to an additional cross-sensitivity of the N2O analyzer to water vapor (0.3 ppb N2O/% H2O). Both effects cause a negative bias of similar magnitude (0.3 nmol m2 s1 N2O flux/mmol m2 s1 H2O flux) in the flux measurements. While the dilution (or density) correction is a well known and routinely applied procedure, the magnitude of the analyzer cross-talk may depend on the specific instrumental setup and should be empirically determined. The comparison with static chamber measurements shows the necessity of the cross-talk correction; otherwise the QCL-AS based eddy covariance system would yield unrealistically large uptake of N2O. ß 2009 Elsevier B.V. All rights reserved.

Keywords: N2O flux Eddy covariance method Quantum cascade laser Field measurements

1. Introduction In recent years operational flux measurement networks have been setup to monitor biosphere–atmosphere exchange of trace gases with the aim to improve our understanding of the underlying processes and to estimate annual exchange rates on regional to global scale. Observations also serve to validate models used to predict climate change related issues (Sto¨ckli et al., 2008). Up to now these flux networks have been limited to CO2, water and energy exchange. The new Integrated Carbon Dioxide Observation System (ICOS) program aims to complement operational flux measurement systems with other important greenhouse gases, such as CH4 and N2O (http://www.icos-infrastructure.ipsl.jussieu.fr). In this paper we focus on the eddy covariance (EC) method to determine net N2O exchange between grassland and the atmosphere. We used a novel, cryogenic free, continuous wave quantum cascade laser based absorption spectrometer (QCL-AS) for flux measurements in the field. We are especially interested in

§ Contribution to the special issue ‘‘CH4 and N2O fluxes’’ to be published in Agricultural and Forest Meteorology. * Corresponding author. Tel.: +41 44 377 7504; fax: +41 44 377 7201. E-mail address: [email protected] (A. Neftel).

0168-1923/$ – see front matter ß 2009 Elsevier B.V. All rights reserved. doi:10.1016/j.agrformet.2009.07.013

the bi-directional nature of the N2O exchange (see Neftel et al., 2007) and whether the precision, accuracy and time response for the reliable measurement of uptake of N2O can be achieved. Exchange of N2O between terrestrial ecosystems and the atmosphere is an important component in the global N2O budget. Evaluation of mitigation options in the area of land management require good process understanding, which must be supported by long-term flux measurements. Many experimental data on N2O flux originate from measurements with closed chambers covering areas usually smaller than 1 m2. Recently a growing number of publications based on eddy covariance measurements integrating over areas between 103 and 105 m2 appeared, because fast and sensitive analytical systems based on absorption spectroscopy are now available (Edwards et al., 2003; Pihlatie et al., 2005; Eugster et al., 2007; Neftel et al., 2007). Either type of approach quantifies the net exchange of N2O between the soil surface and the atmosphere. The net exchange represents the sum of all sources and sinks within the soil profile (Clough et al., 1999, 2005, 2006; Neftel et al., 2000, 2007). Sources of N2O are located in aerobic and anaerobic soil microsites. Production of N2O in the soil results from the microbiological processes of nitrification and denitrification. Nitrifiers operate under aerobic conditions. Denitrification is favored by wet conditions with 70–90% water filled pore space, high nitrate concentrations and warm temperatures.

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Denitrification, most likely the only sink for N2O in soil (Vieten et al., 2008), is almost exclusively an anaerobic process (Zumft, 1997; Zumft and Kroneck, 2007, and references therein). Therefore, denitrification takes place in soil compartments where intensive biological activity and reduced diffusivity result in an O2 deficit. A consequence of the essential diffusion limitation is that denitrifying organisms are more abundant in the centre compared to the periphery of soil aggregates (Stepanov et al., 1997). Reduction of N2O in soil follows Michaelis–Menten kinetics. Nonetheless, halfsaturation concentrations of this reaction are more than an order of magnitude higher than atmospheric N2O concentrations (HoltanHartwig et al., 2000; Vieten et al., 2009). Therefore, it is justified to consider N2O reduction as a first order process in the context of a net flux from atmosphere to soil. Several published datasets contain some data points indicating periods of a net N2O flux from the atmosphere to the soil surface, which we may call N2O uptake flux (Chapuis-Lardy et al., 2007, and references therein). But only in a few cases, these were specifically addressed. One of the first evidences for net N2O uptake by soils in the field was published by Ryden (1981). The global importance of the uptake process is also illustrated by the fact that lower N2O reduction in the soil since pre-industrial times may have contributed to the change in the observed isotopic signature of atmospheric N2O (Conen and Neftel, 2007). Long-term measurements of N2O exchange over agricultural fields have shown that the largest part of seasonally integrated N2O emissions is associated with a few events of short duration following fertilizer application and, to a lesser extent, with the thawing of frozen soil and rain events (Flechard et al., 2005). In between such emission bursts generally very small fluxes are reported (Neftel et al., 2007). During these ‘background’ periods, uptake of N2O have frequently been observed (Flechard et al., 2005; Chapuis-Lardy et al., 2007). Despite the fact that net fluxes during the background periods are very small in either direction, it is important to determine them accurately. Because of their long duration they often make a substantial contribution to the annual N2O budget of a site. Conen and Neftel (2007) estimated that globally the uptake is equivalent to 6% to 30% of the total current soil N2O source. 2. Material and methods 2.1. The experimental site The Oensingen experimental farm site is located in the central Swiss lowlands (78440 E, 478170 N) at an altitude of 450 m m.a.s.l. The site is part of the two large integrated European flux networks CarboEurope (www.carboeurope.org) and NitroEurope (www.nitroeurope.eu). The soil is classified as stagnic cambisol (eutric), with pH 7.3, an average soil organic carbon content of 27 mg C g1 dry matter in the layer 0–30 cm, a clay content of 42% and a C/N ratio of about 9. Soil porosity measurements indicated an average total pore space e0 = 0.56 m3 m3 in the soil horizon 0–30 cm, and also showed a bi-modal pore size distribution with many micropores (pF >4.2) accounting for 58% of e0. The macropores

(pF <2.5) made up 26% of e0. The potential field capacity amounted on average to 0.43 m3 m3. The climate at the site is temperate continental, with an average annual rainfall of about 1200 mm and a mean annual air temperature of 9 8C. Until the year 2007, the study field was under a multi-annual grass-clover mix of seven species. The field was fertilized twice to thrice yearly with cattle slurry and twice yearly with ammonium nitrate (NH4NO3) pellets, altogether amounting to an annual nitrogen application rate of about 230 kg N ha1. There was no grazing; the field was cut mechanically and harvested as hay or silage with varying annual frequencies depending on meteorological conditions, typically four times a year. CO2 and H2O fluxes were routinely measured with an eddy covariance system (LICOR 7500 gas analyzer and Gill sonic anemometer) installed at 1.2 m above the surface (Ammann et al., 2007, 2009). On 18 December 2007 the field was ploughed and left as fallow until the following spring. It was tilled and re-sown on 5 May 2008. Soil temperature was monitored at five depths (2, 5, 10, 30, and 50 cm) while volumetric soil water content was measured at the four lower depths only, using frequency domain reflectometry probes (ThetaProbe ML2x, Delta-T Devices, Cambridge, UK). 2.2. Measurement periods Data are reported from field observations between June and September 2008. Within this interval four specific measurement periods were chosen as listed in Table 1. The periods are related to the management of the field and usually started with a grass cut followed by the application of fertilizer a few days later. As an exception, the third period was chosen because typical ‘background’ conditions without significant emission fluxes were observed even though mineral fertilizer had been applied only 11 days before. 2.3. High frequency N2O measurements N2O measurements were performed with a QCL-AS developed at Aerodyne Research, Inc. (ARI). The instrument differs from the original design (Nelson et al., 2004) mainly by employing the recently available continuous wave QCL as light source, which has some significant advantages over a pulsed QCL (McManus et al., 2006). Furthermore, it is designed for simultaneous measurement of sample gas and frequency lock spectra, which allows an automated procedure to avoid any shift in the laser emission frequency. The spectrometer was equipped with an in-house developed detector setup which is based on a matched pair of thermoelectrically cooled miniature IR-detectors (Vigo System). This results in a totally non-cryogenic instrument that can run unattended for extended time periods. The laser is tuned over the absorption features of N2O, CH4 and a water isotopologue (HDO) at 7.8 mm, as shown in Fig. 1. Laser scanning, signal processing, and quantitative spectral fitting is fully automated through a dedicated PC software (TDL Wintel, ARI). Air samples were analyzed at 8 kPa in a 0.5 l astigmatic multipass absorption cell with a total optical path length of 76 m. The

Table 1 Overview of reported measurement periods and management activities. Start date

End date

Date of cut

Date of fertilizer application

Type and amount of fertilizer

01.07.2008

16.07.2008

28.07.2008

16.08.2008

01.07.08 (12:00) 29.07.08 (11:00)

10.07.08 (11:00) 07.08.08 (11:00)

16.08.2008 09.09.2008

31.08.2008 23.09.2008

10.09.2008 (11:00)

19.09.08 (10:00)

Ammonium nitrate (30 kg N ha1) Ammonium nitrate (30 kg N ha1) ‘‘Background fluxes’’ Cattle manure (35 kg N ha1)

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Fig. 1. Measured (dots) and fitted (line) transmission spectrum of ambient air at typical sampling condition (see details in text). As a comparison, the bottom figure illustrates the abundance weighted line strengths of the trace gases in the spectral region selected for this study. Their spectroscopic parameters were used to simultaneously retrieve the mixing ratios of CH4, N2O and water vapor.

absorption spectra were acquired at 5 kHz and averaged over 10 Hz. Trace gas mixing ratios were retrieved real-time by the TDL Wintel software using the procedures described by Nelson et al. (2004). Absolute concentrations were obtained by calibration with a pressurized air cylinder that was linked to a CMDL standard (Climate Monitoring and Diagnostics Laboratory, NOAA, USA). Since pressurized air does not contain significant amounts of water vapor, the latter could not be calibrated in this way. Instead, it was calibrated on-line by comparison with the open-path LICOR 7500 sensor mentioned above. Hence, the natural abundance weighted HDO mixing ratio was converted into the total water vapor mixing ratio determined by the LICOR 7500. The resulting calibration factor is however influenced by the diurnal fluctuations of dD. Assuming a typical upper limit of 40% for variations in dD, this would induce an error in the derived water mixing ratios of less than 2%. This is lower than the estimated uncertainty of the reference water vapor signal (3%) and thus affects the uncertainty of the QCL-AS derived concentrations and fluxes only to a very minor degree. The precision of the instrument was characterized under field sampling conditions by the Allan variance technique (Werle et al.,

777

Fig. 2. Time series of N2O mixing ratios (10 Hz) measured by the QCL-AS system under field conditions and the associated Allan variance plot.

1993). The corresponding Allan variance plot for N2O is shown in Fig. 2. The resulting values are 0.7 ppb at 1 Hz and a variance minimum of 0.12 ppb after 120 s averaging time. These are conservative estimates because ideally the Allan variance should be determined on samples from a gas tank with constant mixing ratios, a procedure which is, however, inconvenient at the high flow rates used in this setup. During the field measurements, the spectrometer was located in an air conditioned trailer, and samples were drawn by an oil-free vacuum pump (TriScroll 600, Varian) through 30 m PFA tubing (I.D. 6 mm), the tip of which was attached 20 cm from the sonic anemometer (Fig. 3). The inlet filter (MK 360, Munktell, Germany) was slightly heated by flexible heaters (Thermofoil, Minco Inc.) on the metal filter housing. The custom made sampling tube (Hot Tube, Clayborn Lab) was maintained at about 10 8C above ambient temperature by applying a constant current through its heating elements. The air flow rate in the sampling line was 13.5 l min1, resulting in a turbulent flow with a tube transit time (lag time) of about 4 s. This flow rate was regulated by a mass flow controller placed before the analyzer. The flow controller compensated for any flow changes due to filter clogging or variations in ambient

Fig. 3. Schematic of the QCL-AS sampling and optical system employed in the field for eddy covariance measurements (together with the Gill sonic anemometer and the openpath CO2/H2O sensor LI-7500). A small fraction of the laser (QCL) beam is measured by the reference detector (Dref) to lock the spectral position based on the absorption of N2O in the reference cell.

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conditions. A response time (first order, 1/e) of 0.4 s and 0.8 s was determined in the field for N2O and water, respectively, through the entire setup, i.e. from the inlet to the analyzer when switching between ambient air and dry pressurized air. The QCL-AS is able to measure water vapor simultaneously with N2O and CH4. Given this option, water vapor was not removed during the field measurements for several reasons: (i) to achieve >80% water removal at the given sample flow it is necessary to employ a large permeation dryer (e.g. Perma Pure PD-200, 4800 ) and an additional pump for the drying circuit. Using the sample pump to supply purge air to the dryer would significantly reduce the sampling speed and thus the response time of the analyzer. This aspect is common to all laser based instruments that work at reduced sample pressure at which any restriction, such as a drier after the sample cell, implies high pressure drops, thus considerable reduction of the volume flow rate. (ii) Since water vapor is a critical parameter, the drying efficiency must be controlled permanently to account for residual water; especially during summer conditions when the apparent N2O flux (due to dilution) may be larger than the real N2O flux, as shown in Fig. 7. (iii) A drying unit will lead to additional dampening and time lag (see Section 2.4). (iv) The water vapor signal is very welcome as a proxy for the time lag of the often undetectable fluxes of N2O and CH4. We recognize that a sampling setup without a drying unit has potential limitations, because water exhibits both large fluxes and concentration changes which might lead to interferences in concentration and flux values. On the other hand, water data from the same measurement cell allows to study such effects and to evaluate the impact that no or incomplete drying would have on the results. Furthermore, we anticipate flux measurements of other substances (e.g. NO2, HNO3, HCOH) to become possible with the same spectrometer in the near future, but such compounds would be strongly affected by a drying unit. 2.4. Eddy covariance approach Following the EC method, the vertical flux of a trace gas FECraw is calculated as the covariance of the discrete time series of the vertical wind wðtÞ and the concentration c(t) over an averaging period T of typically 30 min with tdel representing the time delay between the vertical wind speed and the scalar concentration: F ECraw ¼ covwc ðt del Þ ¼



 T Dt X T

wðtÞcðt  t del Þ

(1)

t¼0

The two time series have to be adjusted to each other by tdel in order to account for the delayed detection of the trace gas concentration mainly due to the tube transit time. In case a turbulent flux is present, the cross-covariance function shows a clear peak for the characteristic delay time (time lag) of the system. The observed time lag theoretically corresponds to the residence time (Neftel et al., 2007) of the air in the sampling tube that is determined by the flow rate and the tube volume. In practice deviations from the calculated time lag can occur due to various reasons such as variations of the pump speed or wind direction (Leuning and Judd, 1996), non-negligible data acquisition and processing time, or humidity dependent sorption effects on the tube walls (Ibrom et al., 2007). Consequently tdel must be derived with a peak detection routine in the covariance function. There is a caveat: a secure lag determination can only be made if a flux is present that clearly exceeds the detection limit. Because N2O fluxes are most of the time very low, the lag check can be performed either with CO2 or H2O fluxes.

2.4.1. WPL and water vapor cross-talk correction Fluctuations of the air temperature and humidity affect the absolute concentration of trace gases like N2O in the ambient air by thermal expansion and dilution, respectively. Since these fluctuations are also correlated with the vertical wind speed (representing a sensible heat or water vapor flux), their influence has to be removed from the EC trace gas flux by the Webb, Pearman and Leuning (WPL) correction (Webb et al., 1980). A correction due to the sensible heat flux can be omitted in the present case because the long inlet line fully dampened the temperature and the associated density fluctuations. However, in case water vapor is not removed from the sample air, a WPL correction for the correlated dilution effect must be applied:

F WPLcorr ¼ F ECraw þ cF ¯ H2 O ðc in mol mol

1

and Fterms in m s1 ppbÞ

(2)

The QCL-AS measures a water isotopologue (HDO) and thus HDO fluxes are measured by the EC system. The ratio of HDO to H2O flux has been shown to be very constant with relative variations 2  104 (He et al., 2001). Consequently a direct proportionality between the measured HDO flux with the ambient water vapor flux was assumed. As mentioned in Section 2, the QCL-AS measures water vapor simultaneously with N2O. This has the advantage, that every single concentration measurement can be normalized to dry conditions numerically (Eq. (3)), which eliminates the need for the WPL term. cN2 Odry ¼ cN2 Owet

1 1 c in mol mol ð1  cH2 O Þ

(3)

The EC flux of cN2 Odry is independent of F H2 O . In this context it should be noted that the apparent water vapor flux as detected inside the analyzer cell may be reduced due to attenuation in the sampling system. Thus, using ambient water vapor flux – measured by a separate system (possibly with an open-path instrument) – may not be adequate for the WPL correction. As shown in Fig. 1, the absorption features of N2O, CH4 and HDO are well separated, and the simulation used to retrieve the corresponding mixing ratios takes into account every significant absorption line that is given in the HITRAN‘01 database (Rothman et al., 2003) within the relevant spectral range. Therefore, one might assume that there is no cross-sensitivity (often called spectral interference) to other ambient air species. While this assumption holds for most applications, it should be checked carefully in the case of N2O flux measurements for two reasons: (i) water vapor concentrations can vary strongly, and (ii) changes in water vapor are often correlated with vertical wind speed, and any cross-sensitivity of N2O to H2O would thus lead to an apparent N2O flux. In this context, cross-talk (or cross-sensitivity) means any apparent (positive or negative) N2O signal which is solely due to the presence of water vapor and thus indicates a limitation in either the measurement or interpretation of the spectra, independently of the source of this limitation. For these reasons, the effect of increasing humidity on the retrieved N2O concentration was determined experimentally. The QCL-AS was used to simultaneously monitor the mixing ratios of N2O and water vapor of humidified samples from a pressurized air cylinder. The results of this experiment are shown in Fig. 4. It illustrates the density (dilution) effect addressed by the WPL correction and an additional cross-talk between H2O and N2O. The latter is rather small for most analytical applications; however for flux measurements it would induce a flux artifact that is similar in magnitude to the water vapor density effect as shown below. Since no direct spectral interference to water vapor is expected, we consider the observed cross-talk to be due to gas phase molecular interactions causing

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According to the Reynolds averaging rules, the EC flux is then calculated as: F corr  w0 c0corr ¼ w0 c0meas þ bct w0 c0H2 O

(7)

Mathematically, the correction of individual concentration data with Eq. (4) is equivalent to the integral correction in Eq. (7) using the water flux measured by the QCL-AS.

Fig. 4. Effect of varying humidity of the sample air on the measured N2O mixing ratio. The calibration gas from a dry, pressurized air cylinder with 330 ppb N2O was bubbled through water containing bottles at different temperatures and, thus, its water content gradually increased. The open symbols and the lower regression line represent mixing ratios relative to wet air (directly proportional to the QCL-AS signal. For the closed symbols (upper regression line), the N2O mixing ratios were normalized to dry conditions based on the simultaneously measured water mixing ratios. The associated prediction bands defined at 1s confidence level indicate the uncertainty of the linear fits.

additional line broadening. Such gas-matrix dependent line broadening is not implemented in the current retrieval algorithm. This is because the HITRAN database contains only the dry air broadened half-width (gair) and because it would be particularly difficult to apply water broadening corrections on real-time data. More detailed laboratory investigations of spectroscopic parameters, especially water pressure broadening coefficients, are needed to clarify the issue. Our hypothesis is further confirmed by measurements of CH4 with the same instrument showing a similar effect of humidity on the retrieved CH4 concentrations (data not shown). It should be recognized that all IR gas analyzers may show some response to the gas-matrix, especially to water vapor. This effect is expected to be more pronounced for instruments having very narrow laser line width (0.002 cm1), such as continuous wave QCL or lead salt lasers, and less significant for pulsed QCL (0.015 cm1) since their lower spectral resolution tends to make them less sensitive to spectral details. Based on this study, we would suggest that for EC measurements of small fluxes (e.g. N2O), such cross-talk effects should be quantified empirically for any IR trace gas analyzer, independently of the employed wavelength and spectroscopic technique if the sample is not dried prior to analysis In this study an empirical approach is used to account for the above mentioned effect, treating it similarly to the water vapor density correction. The corrected scalar concentrations are given by: ccorr ¼ cmeas þ bct cH2 O

(4)

The coefficient bct corresponds to the slope of the linear dependence of the trace gas concentration on the water vapor concentration (Fig. 4). Applying the Reynolds decomposition c ¼ c¯ þ c0

(5)

(see e.g. Webb et al., 1980) on all concentration quantities in Eq. (4) results in: c¯corr þ c0corr ¼ c¯meas þ c0meas þ bct ðc¯H2 O þ c0H2 O Þ

(6)

2.4.2. Correction for tube dampening Closed-path instruments with inlet lines will exhibit a dampening of fast concentration variations depending on the geometry of the tube, the pump speed and the meteorological conditions. Many approaches to correct for this effect have been published (e.g. Moore, 1986; Ammann et al., 2006; Massman and Ibrom, 2008). We applied an empirical approach described in Ammann et al. (2006) that is based on a comparison of the cumulative spectra of the scalar flux of interest with the heat flux derived from the high frequency sonic anemometer data. The resulting dampening correction factor for the water vapor fluxes could be parameterized as a function of the wind speed u (m s1). We restricted the analysis to relative humidities below 80%. Dcorr ¼

1 ð0:908  0:0688uÞ

(8)

A specific dampening correction for the N2O flux could not be derived in a suitable way, because the scatter of the individual data was too large. However, it is expected that the dampening correction for water vapor is an upper limit for N2O, because wall interactions of N2O molecules are smaller. Ammann et al. (2006), e.g., found a 5% smaller dampening for methanol compared to water vapor for a similar tube length. This difference is considered to be small compared to other potential biases in the N2O flux. Therefore Eq. (8) was also applied for the N2O flux here. 2.4.3. Precision of eddy covariance fluxes The precision of an individual EC flux measurement was calculated as the standard deviation of the covariance function (according to Eq. (1)) far outside of the true time lag (200 s < tdel < 50 s;and +50 s < tdel < +200 s), as first proposed by Wienhold et al. (1995). This error approach takes into account variations in the covariance function that potentially disturb the flux determination. To get an estimate for the precision of the N2O fluxes, the mean error of individual flux data was calculated for the third measurement period (16–31 August 2008) with expected consistently low fluxes and amounted to 0.17 nmol m2 s1. This value is also regarded as the statistical detection limit of the QCLAS for N2O flux measurements. 2.4.4. Quality criteria Flux results have been quality checked with (a) an ogive shape criterion and (b) a covariance function offset criterion: (a) The sensible heat flux must show a smooth and monotonic ogive (Neftel et al., 2007). This criterion eliminates cases with intermittent or non-turbulent conditions mainly due to weak winds. (b) Slow variations on the order of minutes in the EC time series (‘‘pink noise’’) may lead to offsets in the covariance function in case a stochastic correlation between the vertical wind variations and the scalar concentration exists. The offset at the position of the covariance peak (true lag) was determined by a linear interpolation through the values of the covariance function at some distance from the true lag value (23 to 18 s and +18 to + 23 s). For the offset in the covariance function an acceptable range of 0.4 nmol m2 s1 for N2O fluxes and

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0.2 mmol m2 s1 for H2O fluxes was defined. This criterion rejects fluxes that are strongly influenced by low frequency bias. 2.5. Static chamber system N2O fluxes were measured using opaque static chambers (Flechard et al., 2005). Four automated, stainless steel chambers (length = width = 300 mm, height = 250 mm), custom built by the Physics Department of the University of Bern, provided flux measurements every 2 h each. The chambers were equipped with a small fan for internal air mixing and were mounted atop a PVC frame that was driven 5 cm into the soil. The chambers were closed for 15 min for each flux measurement after 5 min of tube flushing. Concentrations of N2O in the headspace were determined once per minute using non-dispersive infrared absorption technique (N2O: Thermo Environmental Instruments) in a closed loop with continuous sample air flow. The dew point was stabilized to 14 8C with a Nafion dryer (Perma Pure Inc., model MD 110-72). In order to control both the accuracy and linearity of the concentration measurements, an online calibration was carried out every 6– 12 h with three certified gas standards (concentration ranges: 300–5000 ppb N2O). The measurements were fully automated and computer-controlled, allowing a continuous monitoring of the N2O fluxes. The N2O and CO2 flux (F) between the soil/vegetation system and the atmosphere was determined from the concentration change of N2O and CO2 over time in the closed static chamber:

Fig. 5. Time series (16–31 August) of the water vapor fluxes measured with the open-path LICOR 7500 system (triangles) and the QCL-AS (circles). Only fluxes meeting the quality criteria mentioned in Section 2.4.4 are shown.

measurement periods. This indicates that the air flow through the sampling tube remained constant. Consequently a fixed lag of 4.0 s was used to calculate the N2O flux. The few cases with clearly detectable N2O fluxes confirmed this stable behavior, the corresponding lags varied less than 0.4 s. Fluxes calculated with this fixed lag did not differ significantly from those determined by searching the maximum of the individual covariance functions.

where V denotes the headspace volume, A the surface area, and H the height of the chamber. Flux data were rejected from the dataset if the correlation coefficient (r2) of the linear regression for the CO2 concentration as a function of time dropped below 0.95. This was an indication that e.g. the chamber was not properly sealed or that soil cracks during droughts induced lateral air leakage in the soil and undermined the use of a chamber.

3.1.2. WPL and water vapor cross-talk corrections To demonstrate the importance of the water vapor related corrections, again the background measurement period from August 16th to August 31st (see Table 1) was chosen where the chamber measurements consistently showed N2O flux values below 0.4 nmol m2 s1. The raw N2O flux by the EC system without any water vapor correction exhibits a clear negative correlation with the water vapor flux as demonstrated in Fig. 7. Applying the WPL density correction alone reduced this dependence by 60% still leaving a negative correlation. Finally the crosstalk correction changed the water flux dependence of the N2O flux to a non-significant correlation.

3. Results

3.2. Eddy covariance and chamber flux data

3.1. Eddy covariance flux evaluation

During the measurement periods smaller operation gaps and rejection due to quality criteria (Section 2.4.4) resulted in final data coverage of 42% for the EC fluxes. This roughly corresponds to the long-term average of accepted fluxes for CO2 and H2O at the same

V DC DC ¼H F¼ A Dt Dt

(9)

3.1.1. Lag determination Large water vapor fluxes over the grassland allows a detailed investigation of the lag behavior. Exemplarily the third measurement period (see Table 1) covering the time from August 16th to August 31st is analyzed here. Fig. 5 shows the time series of the water vapor flux measured by the QCL-AS together with the water flux from the open-path LICOR system. The comparison of the water vapor fluxes obtained by the two methods shows a good overall correspondence (linear regression slope = 1.0018) but a rather large scatter for the individual data points (r2 = 0.78). The lag time for the H2O flux obtained with the QCL-AS is displayed in Fig. 6 together with the relative humidity. As mentioned in Section 2.4 it mainly represents the residence time of the air in the long sampling tube. The results show a clear diurnal pattern with longer lag times for higher relative humidity, which would be consistent with stronger attenuation at higher humidity as shown by Massman and Ibrom (2008). Whenever the relative humidity was decreasing, the lag time for water vapor was also decreasing and vice versa. The lag time for very humid conditions (mainly in the morning hours) was up to 3 s longer than for the drier midday conditions. For relative humidity smaller than 70%, the lag time did not show a systematic drift during the four

Fig. 6. Lag time (black dots) between vertical wind and water vapor measured by the QCL-AS based EC system as derived from the position of the maximum in the covariance function of both quantities; relative humidity (open diamonds) is plotted on the second ordinate.

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fluxes was calculated by Gaussian error propagation rules from the above described Wienhold error for the raw N2O flux in combination with estimated uncertainties of 30% for the applied WPL correction and 50% for the correction of the water vapor cross-talk. 4. Discussion 4.1. Mean N2O flux derived from the chamber measurements

Fig. 7. N2O flux versus water vapor flux both derived by QCL-AS eddy covariance measurements for the third observation period (see Table 1); light gray squares: no density correction, dark gray diamonds: with only integral WPL correction applied (Eq. (2)), black diamonds: with integral WPL and cross-talk correction applied (Eqs. (2) and (7)). The solid lines indicate the results of least-squared linear regressions for the three datasets. For the fully corrected case, the dotted curves indicate the 2s confidence interval of the linear regression.

site (Ammann et al., 2007). Rejection was predominantly due to breakdown of turbulence during calm nighttime conditions. Chamber flux data have been aggregated to 4-h mean values determined by the time resolution of the static chamber measurements. Fig. 8a–d give an overview of all N2O fluxes measured by EC and static chambers for the four periods listed in Table 1, together with important environmental parameters. For the chamber data the median value of the four chambers is shown together with the extreme values. The three periods related to grass cuts and fertilizer applications (Fig. 8a, b and d) all show a similar behavior. Following the grass cut but before fertilizer application an N2O emission event occurred that was generally more pronounced in the EC field scale measurements than in the chamber data. The largest emission recorded by the EC system occurred at noon time on July 3rd and lasted only a few hours (Fig. 8a). The opposite situation was observed after fertilization. Rain triggered N2O emissions that are more clearly visible in the chamber data. As can be seen in Fig. 8b, N2O emissions started 5 days after the application of fertilizer when rainfall occurred. These enhanced emissions lasted a few days before dropping back to the background level with fluxes around zero (absolute values not exceeding 0.4 nmol m2 s1). Unfortunately technical failure of the QCL-AS occurred several times shortly after management events. These failures were later identified as overheating of the electronics power supply and condensation in the optical module. An additional fan on the DC power unit and purging the optical module with dry air solved both problems. A generally decreasing trend of the emission levels was observed after the peak in mid July towards autumn that paralleled the establishment of the recently re-sown grass vegetation. During some phases, the chamber data showed large persistent differences between the individual chamber locations as illustrated by the large maximum–minimum ranges. They indicate that even in homogeneously managed grassland, there exist hotspots with three to four times higher local emission levels compared to the field average. One chamber obviously was located on such a hotspot during the second half of July. After a rain event this chamber consistently showed one order of magnitude larger emission rates than all other chambers. The large spatial inhomogeneity complicates comparison between EC and chamber approaches. Fig. 8c shows a typical background situation with generally small fluxes. The indicated uncertainty range of the individual EC

The overall mean N2O emission measured with the static chambers over the reported observation periods was 1.2 nmol m2 s1,which corresponds to a cumulative emission of 3.1 kg N2O–N ha1 for the entire four month period. This value is at the high end of the range of previously measured fluxes at the Oensingen site since the start of measurements in 2002 (see Flechard et al., 2005). The main reason is probably that the field has been tilled in December 2007 and re-sown in early May 2008. Tillage can initialize a strong mineralization activity while reducing the sink for mineral N previously provided by the sward. As a result, emissions of N2O in the season following the event can be substantially larger than the preceding season (Davies et al., 2001). In the reported measurement period no significant and persistent net uptake have been recorded with the automated chamber system. This is different from previous years where such periods with persistent small uptake fluxes had been measured (Flechard et al., 2005; Neftel et al., 2007). Again, this might be attributed to the ploughing in December 2007 which caused enhanced mineralization and consequently higher nitrate availability. The mean flux determined with EC fluxes over the same period was 0.56 nmol m2 s1 thus only half of the value determined from the chambers. This difference is not surprising giving the typically large variability of static chamber measurements. 4.2. Variations in N2O emission Observed N2O emission events are mainly connected to fertilizing, cut, and rain events. During the reported measuring periods the field was fertilized three times, twice with mineral fertilizer on July 10th and August 7th and once with organic fertilizer on September 19th. Both, chambers and EC measurements, showed the expected sharp increase in emission after the addition of mineral nitrogen by fertilization, sometimes with a delay until the next rain event. Usually, these emission peaks were relatively short and dropped off again within a few days to background level below 0.4 nmol m2 s1. It is somewhat surprising and counterintuitive that the fluxes observed after cutting but before fertilization also show emission peaks that partly exceed the emissions after the application of fertilizer. Such peaks were always related to water input. Denitrification is almost exclusively an anaerobic and endothermic process (Zumft, 1997, and references therein). It comprises the stepwise reduction of NO3 to NO2, NO, N2O and eventually N2. It takes place in biologically active soil aggregates, where oxygen concentrations can drop to zero within a couple of mm (Hojberg et al., 1994). The fraction of anaerobic volume in a soil is governed by the soil water content (Arah and Vinten, 1995) limiting oxygen diffusion, and labile carbon concentrations stimulating oxygen demand (Rappoldt and Crawford, 1999). The ratio of N2O to N2 that is released from the aggregates into the open pore space increases with increasing nitrate concentrations. Elevated N2O emissions require therefore both high soil humidity and a supply of nitrate and labile carbon to the actively denitrifying micro sites where N2O production takes place. Water is needed as a

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Fig. 8. (a) Time series of environmental parameters and N2O fluxes for the first measurement period (see Table 1). The upper two panels show precipitation, wind speed, soil temperature at 5 cm depth and the fraction of water filled pore space at 10 cm depth. In the lower panel the N2O fluxes measured with the QCL-AS EC system and the static

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carrier for these substrates to reach such sites. Cutting the vegetation in a managed grassland most likely results in a defoliation-induced stimulation of labile carbon exudation from roots and a resulting acceleration of nitrogen mineralization in the rhizosphere, of which a five-fold increase has been observed, e.g. by Hamilton et al. (2008). As a consequence of more rapid mineralization, nitrate concentrations will increase. At the soil surface, shoot residues may have similar effects. In addition, microbial biomass in the rhizosphere may grow (Hamilton et al., 2008) that is largely unable to reduce N2O to N2 (Che`neby et al., 2004) and produces therefore more N2O than would be expected from a similar growth in biomass and activity in the bulk soil. Rainfall following a grass cut increases soil water content, transports the labile carbon and nitrate to denitrifying microsites and increases the latter by reducing oxygen diffusion. Fertilizer was applied with a time lag of 7–10 days relative to the grass cuts. This was enough time for the vegetation to start the re-growth. Consequently the demand for nitrate by the plants must have been increased compared to the situation immediately after the cut and probably led to a stronger competition with N2O producing bacteria for nitrate. Also, increased carbon exudation from roots following defoliation may have ceased by then. This situation could have dampened the fertilizer related emission peak. Thus, delaying the application of fertilizer until the regrowth has started possibly constitutes a cheap option to reduce N2O emissions. In the time series of N2O fluxes by the QCL-AS based EC technique a sharp peak with a maximum value of 16 nmol m2 s1 occurred on July 3rd and lasted until the same evening. During this period chamber fluxes failed the quality test and they consequently do not appear in Fig. 8a. A detailed inspection of the N2O chamber concentrations showed that chamber fluxes have certainly not exceeded a level of 3 nmol m2 s1 while the EC data peaked at almost 20 nmol m2 s1. This may be explained by the preceding grass cut on July 1st. It is likely that more harvest residues than normal remained on the field, but not within the frames of the static chambers. In the morning of July 3rd a short intensive rain event occurred (7 mm rain in 2 h) after a longer dry period. The water did not penetrate deeper into the soil as indicated by the unchanged WFPS value (Fig. 8a). However, the wetting of the surface layer may have triggered a strong N2O production, since the grass residues provided an easily available carbon and nitrogen source for nitrifiers and denitrifiers (that was not available at the surfaces covered by the chambers). N2O production can also be strongly enhanced due to earthworm activities, especially when they have access to grass residues (Lubbers et al., 2009). A similar feature but less pronounced occurred shortly after the two other cuts, where EC fluxes showed a pronounced increase exceeding the chamber fluxes. It cannot be excluded the rain events of short duration.

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Fig. 9. Lag time of N2O fluxes (black triangles) and water vapor fluxes (grey circles) together with relative humidity (open diamonds).

but that CO2 – an inert gas like N2O – showed much less lag variations associated with the relative humidity. Since, in the present study, no clearly detectable N2O flux was present most of the time, a systematic analysis of the N2O lag behavior could not be made. The few highly significant fluxes showed only small differences up to 0.25 s between the N2O and H2O lags for relative humidity <70% (Fig. 9). Similar to the results found by Ibrom et al. (2007) the inert gas N2O showed no significant change in lag for relative humidity exceeding 70%. Ibrom et al. (2007) and Massman and Ibrom (2008) demonstrated that the integral WPL correction of a trace gas flux in closed-path systems must be based on the water vapor flux calculated with the lag of that specific trace gas, but not with the effective lag of the water vapor flux itself. In their study on a forest tower in Denmark, lag differences between CO2 and H2O were strongly dependent on relative humidity with values around 1 s for dry conditions and as high as 7 s for relative humidity approaching 100%. If the WPL correction was done with the ‘true’ water flux (using its individual optimum time lag) this resulted in a systematic overcorrection. Over a year, this effect could bias the cumulative CO2 exchange by as much as 20% as shown by Ibrom et al. (2007). This bias depends on the specific setup and cannot be generalized. For our setup the differences between water vapor fluxes calculated with the expected constant lag were on average only 1% smaller compared to the fluxes calculated with the lag derived from the maximum of the covariance function. Largest deviations were about 0.2 nmol m2 s1 and occurred in the morning hours when relative humidity started to decrease. The corresponding systematic error introduced in the WPL correction is very small and was neglected.

4.3. Eddy covariance flux calculation and correction 4.3.1. Lag determination For water vapor, the lag time of detection in the QCL-AS eddy covariance system was found to depend on the relative humidity of the sample air, with longer lag times for higher relative humidity, and on the temporal change in the humidity conditions. A similar observation is reported by Ibrom et al. (2007). They found that lag times of H2O were related to the relative humidity

4.3.2. Correction due to water cross-talk To investigate the effect of water vapor related interferences, data of the third observation period were chosen, for which static chamber measurements showed generally small background N2O fluxes below 0.4 nmol m2 s1 and thus below the precision of the EC flux measurements. The chamber fluxes of this period were not correlated with simultaneous water vapor fluxes. For these conditions we hypothesize that the EC N2O fluxes were not

chamber system are shown. QCL-AS fluxes are half hour values. Chamber data are displayed as 4-h median values and the range of all flux values of the four chambers is indicated as dashed line. Note that Y-scale is different in each graph of Fig. 8(a)–(d). (b) Same as Fig. 8a but for the second measurement period (see Table 1). (c) Same as Fig. 8a but for the third measurement period (see Table 1) with ‘‘background’’ N2O fluxes. The indicated uncertainty range of the individual fluxes was calculated by Gaussian error propagation rules from the error of the raw flux (according to Wienhold et al., 1995) and estimated uncertainties for the WPL correction of 30% and for the cross talk correction of 50%. (d) Same as Fig. 8a but for the fourth measurement period (see Table 1).

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significantly different from zero and independent of the water vapor flux. As expected, the uncorrected N2O flux showed a strong negative correlation with the QCL-AS water vapor flux (Fig. 7). Applying only the standard WPL correction (Eq. (4), using the simultaneously measured QCL-AS water vapor flux) reduced the negative slope of the linear dependence, but a significant negative correlation remained. As explained below, N2O uptake fluxes lower than 0.6 nmol m2 s1 are physically implausible, which supports the necessity of an additional correction for the water vapor cross-talk effect as discussed in Section 2.4.1. Application of the cross-talk correction led to a non-significant dependence of the N2O flux from the water vapor flux. However, a systematic bias on the order of 0.2 nmol m2 s1 due to the uncertainty of the cross-talk correction cannot be excluded. Thus, the water vapor cross-talk effect must be carefully characterized for any reliable long-term monitoring of N2O fluxes with a QCL-AS or any other optical analyzer. 4.4. Estimating an upper limit for N2O uptake flux There is a vast body of data on CH4 flux from the atmosphere to the soil which is driven by methanotrophic organisms within the soil profile. Such fluxes are much larger than uptake fluxes of N2O, are quantifiable with less uncertainty, and thus provide more trustworthy values. Uptake processes of both CH4 and N2O in the soil can be considered to follow first order kinetics around the corresponding atmospheric concentrations. Both processes are limited by the diffusion of the consumed gas species from the atmosphere to the site of reaction within the soil. The diffusion limitation is stronger for the anaerobic process of N2O reduction because this is taking place in poorly aerated microsites, whereas the aerobic process of CH4 oxidation takes place in better aerated sites within a soil profile. Consequently, a conservative upper limit for N2O uptake fluxes can be estimated by scaling the maximum reported CH4 uptake fluxes with the atmospheric concentrations of CH4 and N2O. Maximum rates of CH4 oxidation reported by a study covering different types of land use in six European countries (Smith et al., 2000) were close to 200 mg CH4 m2 h1 or 3.5 nmol CH4 m2 s1. The molar ratio of N2O to CH4 in atmospheric air is about 0.17. Thus, the upper range limit for N2O uptake fluxes is most likely below 0.17  3.5 = 0.60 nmol m2 s1. We postulate that reported persistent uptake fluxes exceeding this value are likely to be biased by a systematic error. Omitting the water cross-talk on the N2O concentration measurements in this study, for example, yielded far too large uptake fluxes that would have been ruled out by this criterion. Individual measurements, of course, may show larger negative values due to random errors of the measurement system. 5. Conclusions A new QCL-AS has been operated for EC flux measurements of N2O at an intensively managed grassland site in parallel with automated static chambers to gain information on the long-term performance in field operation. It was found that large emission fluxes can reliably be detected with the system, while small fluxes may easily be biased by its sensitivity to water vapor, exceeding the statistical precision of the EC flux measurements. One part of the necessary correction is associated with correlated density fluctuations of the dry air induced by the water vapor flux (WPL correction). Consequently, the precision of the correction is limited by the precision of the water flux measurement. In addition, the presence of water vapor has a cross-talk effect on the trace gas concentration measured by QCL-AS. The corresponding correction of the N2O flux is in the same order of magnitude as the WPL

correction. Drying of the air samples is a valuable alternative, but it affects or prevents the measurement of many reactive trace gases and it induces additional cost, power consumption and signal dampening. Furthermore, the drying achieved in practice is often incomplete, and it has the disadvantage that a reliable lag determination is no more possible when trace gas fluxes are below the detection limit. The results presented here clearly show the importance of careful investigation of possible gas-matrix effects – especially of water vapor – for any infrared trace gas analyzer when dealing with small trace gas fluxes. The N2O exchange at the study site is typically characterized by long periods of small fluxes of either direction interrupted by a few bursts of N2O emission generally related to management events or triggered by rain events. N2O emissions following the grass cut events were in the same order of magnitude as the emissions directly related to the application of fertilizer. It has to be further investigated whether a delay of several days between the cut and the fertilizer application causes a significant decrease in the emission of N2O and would therefore be an efficient and cheap mitigation of N2O emissions.

Acknowledgements We thank the two anonymous reviewers for their very helpful suggestions and comments. This study was supported by the EU project NitroEurope-IP (Contract 017841), funded under the EC 6th Framework Programme for Research and Technological Development and by the COST action 729 project ‘‘Assessment of nitrogen biosphere–atmosphere exchange based on novel quantum cascade laser technology’’ SBF Nr. C06.0017

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