Applicability of a closed-path quantum cascade laser spectrometer for eddy covariance (EC) flux measurements of nitric oxide (NO) over a cropland during a low emission period

Agricultural and Forest Meteorology 282–283 (2020) 107855

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Applicability of a closed-path quantum cascade laser spectrometer for eddy covariance (EC) flux measurements of nitric oxide (NO) over a cropland during a low emission period

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Kai Wanga, Dong Wanga,b, Xunhua Zhenga,b, , David D. Nelsonc a

State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences (IAP-CAS), Beijing 100029, China b College of Earth and Planetary Science, University of Chinese Academy of Sciences, Beijing 100049, China c Aerodyne Research, Inc., Billerica, Massachusetts 01821, United States

A R T I C LE I N FO

A B S T R A C T

Keywords: Nitric oxide Eddy covariance Turbulent flux Quantum cascade laser Flux detection limit

Croplands are important sources of atmospheric nitric oxide (NO). However, high-frequency measurements of NO fluxes over croplands using the eddy covariance (EC) technique are still scarce, mainly due to instrumental limitation. In this study, a closed-path NO analyzer based on a quantum cascade laser (QCL) absorption spectrometer was employed for EC flux measurements over a subtropical vegetable field during a two-month summer period with the lowest NO emission intensity of the year. The purpose was to investigate the detection limit of the EC system based on this NO analyzer and evaluate its applicability for measuring the turbulent fluxes of NO under field conditions. The performance of the analyzer was stable, showing an average precision (0.1 s) of 0.338 nmol mol−1 and a corresponding flux detection limit of 5.6 μg N m−2 h−1 at the 95% confidence interval. The measured turbulent NO fluxes ranged from −7.1 to 61.4 μg N m−2 h−1 (median: 3.5 μg N m−2 h−1), with a relative random error of 386% before field ploughing and 76% thereafter. The systematic errors due to the highfrequency loss and the use of lag times of carbon dioxide for NO flux calculation were estimated at 12% and 3%, respectively. During the measurement period, 37% of the observed half-hourly fluxes were larger than the detection limit; the magnitude of these fluxes is comparable with that measured by the static chambers. Nevertheless, this EC system could be still qualified for measuring turbulent NO fluxes over common croplands if the flux averaged at daily or longer timescales are of interest, because either flux detection limit or random error would decrease by an order of n , wherein n is the number of half-hourly fluxes being taken for averages. This study shows that the closed-path dual-QCL analyzer could be an effective option for EC measurements of turbulent NO fluxes with the advantages of (i) stable performance, (ii) high precision and fast response, and (iii) feasible instrumental maintenance for long-term field measurements. However, the observed turbulent fluxes still underestimated the soil NO emissions likely due to chemical reaction loss of NO below the sensor height. Further studies are necessary to address this systematic error.

1. Introduction Nitric oxide (NO) is a chemically reactive gas in the atmosphere usually linked with nitrogen dioxide (NO2) as nitrogen oxides (NOx), since conversion from NO to NO2 as well as NO2 photolysis to NO are both rapid processes (Seinfeld and Pandis, 2006). Nitrogen oxides are well known for their major role in the photochemical formation of tropospheric ozone (O3), a toxic gas for humans, animals and plants (Fowler et al., 1998). Ozone in the troposphere is also an important greenhouse gas, contributing to nearly 25% of the anthropogenic net

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radiative forcing (IPCC, 2007). Although the major anthropogenic source of atmospheric NO is combustion for energy, agricultural soils have been identified as important sources too, with the source strength being closely linked to the use of nitrogen fertilizers. The Intergovernmental Panel on Climate Change reported an estimate of 3.7 Tg N yr−1 for the anthropogenic emissions of NO from soils due to nitrogen fertilizer application in agriculture, which accounted for approximately 10% of the global NO budget (IPCC, 2013). Soil NO emissions are closely related to the microbial processes of nitrification and denitrification (Williams et al., 1992), with autotrophic

Correspondence author. E-mail address: [email protected] (X. Zheng).

https://doi.org/10.1016/j.agrformet.2019.107855 Received 5 June 2019; Received in revised form 21 November 2019; Accepted 23 November 2019 0168-1923/ © 2019 Elsevier B.V. All rights reserved.

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Merbold et al., 2014; Wang et al., 2016). This type of instruments measures gas concentrations based on rapid laser frequency sweeps (1 − 5 kHz) and multiple-pass absorption cells (76 m), thus being able to report the concentrations of independent air samples with high precision at a frequency of 10 Hz (McManus et al., 2010; Nelson et al., 2006). Moreover, the lasers equipped in the ARI instruments can be customized, enabling users to measure their target gas species such as NO, NO2, O3 and many others. For instance, ARI can manufacture closed-path NO analyzer with precision (1 σ noise) of 0.316 ppbv at the integration time of 0.1 s (adapted from the instrumental specification on http://www.aerodyne.com/products/mini-laser-trace-gas-monitor). So far, however, no one has reported the performance of such a laser spectrometer in EC measurement of NO fluxes under field conditions. In this paper, a customized dual-QCL gas analyzer (QC-TILDASDUAL, ARI, USA) was assembled in an EC system for NO flux measurements over a typical subtropical vegetable field in the central China. This is the first attempt to measure NO fluxes over a terrestrial ecosystem by using a closed-path QCL analyzer. The purpose is to evaluate the performance and applicability of the EC system for measuring turbulent NO fluxes at the sensor height. Flux losses and corrections due to the chemical reactions below the measurement height will not be addressed in this study. Therefore, the words “NO flux” mentioned hereafter refers to turbulent NO flux measured at the sensor height, rather than the flux at the soil surface. To investigate the detection limit of the instrument under field conditions, the experiments were conducted during a two-month summer period with the lowest NO emission intensity. Besides, NO fluxes were also measured using the static chamber method. Comparison between the EC and chamber fluxes was used to validate the reliability of the NO flux measurement using the QCL analyzer.

nitrification being most likely the major source process of NO emission from most agricultural soils (e.g., Bollmann and Conrad, 1998; Dunfield and Knowles, 1999). Therefore, in rural regions with intense use of ammonium-based fertilizers, NO emissions from soils may be comparable to other local anthropogenic sources (e.g., ButterbachBahl et al., 2009; Molina-Herrera et al., 2017). Despite the importance of environmental effects of NO, the understanding of NO exchanges between terrestrial ecosystems and the atmosphere is still very limited. At present, there are a few commonly accepted methods for the measurement of NO emissions from soils: a) use of the static chamber methods (e.g., Liu et al., 2010; Yao et al., 2015; Zheng et al., 2003), b) use of dynamic chamber approaches (e.g., Butterbach-Bahl et al., 1997; Gut et al., 2002; Medinets et al., 2016), or c) use of micro-meteorological methods such as the flux-gradient (e.g., Stella et al., 2012; Taylor et al., 1999; Watt et al., 2004) or eddy covariance (EC) method (e.g., Rummel et al., 2002; Stella et al., 2013; Vuolo et al., 2017). Each method has its own advantages and disadvantages. The chamber-based methods are simple in principle and applicable for various environments and situations. However, the measured fluxes have significant biases due to disturbed environmental conditions and limited sampling frequency and spatial replicates (Liu et al., 2010; Medinets et al., 2016). The flux-gradient method is a non-destructive method and the fluxes are calculated from gas concentrations measured at multiple heights and eddy diffusion coefficient along the concentration gradient (Fowler and Duyzer, 1989). Despite less requirement for time resolution (half-hourly average) of concentration measurements, this method also has limitation due to difficulties in quantifying flux divergence errors caused by chemical reactions of NO (e.g., de Arellano and Duynkerke, 1992) as well as uncertainties in the determination of eddy diffusion coefficient (e.g., Flesch et al., 2002). The EC technique is the most direct and least empirical method for measuring energy and mass exchanges between land surface and atmosphere (Baldocchi, 2003). This method has been successfully applied for NO flux measurements in a limited number of studies (Civerolo and Dickerson, 1998; Delany et al., 1986; Finco et al., 2018; Gao et al., 1996; Geddes and Murphy 2014; Lee et al., 2015; Min et al., 2014; Rummel et al., 2002; Stella et al., 2013; Vuolo et al., 2017). It should be noted that NO fluxes measured by the EC method represent the NO exchanges crossing the horizontal plane at the measurement height. NO losses due to chemical reactions below the sensor height need to be corrected if one wants to acquire NO emissions at the soil surface. In the above-mentioned EC studies, we found all of them had used a NO analyzer based on chemiluminescent detector (CLD), which measure NO concentration based on the number of emitted photons from excited NO2 as the product of NO oxidation by O3 in the detection cell. The precision (1 σ noise) of a CLD could be 0.07 nmol mol−1 (or ppbv) at the integration time of 0.1 s (e.g., Rummel et al., 2002), indicating that an EC system with such an analyzer is capable of detecting extremely low NO fluxes of around 1.0 μg N m−2 h−1 at the 95% confidence interval. However, CLDs usually have a minimum response time of 0.2 − 0.5 s (e.g., Ammann et al., 2012; Rummel et al., 2002; Stella et al., 2013). It means that this type of instruments could acquire NO concentration of independent air samples at a frequency of up to 2 − 5 Hz, although 10 Hz data can be recorded by integrating the photons over 0.1 s. This may lead to large high-frequency flux correction if the sensors are placed close to ground, where more fluxes are transferred by small eddies (Foken and Oncley, 1995). In the recent years, commercial closed-path gas analyzers based on the quantum cascade laser (QCL) absorption spectroscopy technique (e.g., QC-TILDAS-76, Aerodyne Research Inc. (ARI), USA) have become available for EC flux measurements of atmospheric trace gasses such as nitrous oxide (N2O), methane and ammonia (e.g., Ferrara et al., 2012;

2. Materials and methods 2.1. Descriptions of experimental field site Flux measurements were carried out from May 16 to July 14, 2015 at a vegetable field site (29°30′21.24′′N, 112°53′42.95′′E) in the suburb of Yueyang, Hunan, China. The field site is subject to a subtropical monsoon climate with a hot and rainy summer and a temperate winter. In 2015, the mean annual temperature was 15.9 °C while the annual precipitation was 1508 mm. The dominant wind directions of the site are north and south. The surface soil (0 − 20 cm) of the experimental field is a silty loam, consisting of 8% clay (< 0.002 mm), 78% silt (0.002−0.05 mm) and 14% sand (0.05−0.2 mm), with a pH value of 7.8 and an average bulk density of 1.3 g cm–3. The contents of total nitrogen and soil organic carbon of the surface soil were around 1.1 g kg–1 and 8.8 g kg–1, respectively. The soil parameters presented above were obtained by analyzing the soil samples collected during the measurement period. Fig. 1a is an aerial photograph of the experimental field taken on August 21, 2015. Although it was not the image during the measurement period, it represents the typical field situation, i.e., a large agricultural field divided into many plots in a ribbon pattern. The size of each plot was 6 − 15 m along the direction of 20° and approximately 100 m along the direction of 110 o. At the left and right border of the field there was a road and an irrigation channel, respectively. The fields beyond the two borders had similar landuse as the experimental field. During the measurement period, the positions of the EC mast and the air-conditioned cabin remained the same as shown in Fig. 1a. Since at least 2011, the plots near the EC mast had been cultivated with hot pepper (Capsicum annuum L.) and cabbage (Brassica oleracea L. var. capitata L.) in two consecutive seasons every year, which normally started in March and August, respectively. Nitrogen fertilizers were applied at rates of 300−400 kg N ha−1 at the beginning of each vegetable growing seasons. These field plots were managed by different local farmers, thus led to differences in management practices, such as 2

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Fig. 1. Aerial photograph of the experimental field at the beginning of the cabbage season in 2015 (a) and frequency of wind direction during the measurement period (b). In panel a, the origin represents the location of the eddy covariance mast and the blue object the cabin housing the gas analyzer and associated devices; the white dashed lines illustrate the wind direction sectors of 0 − 60° and 150−210°, where the accepted turbulent fluxes of nitric oxide were coming from; the white squares represent the locations of static chamber measurements. In panel b, the north direction was set as zero.

located to the east of the EC mast so as to avoid flow distortion in the dominant wind directions. The sampling line was a 10.2 m long Teflon tube with an inner diameter of 6.4 mm. The tube inlet was fixed to the east of geometrical center of the sonic anemometer with a horizontal distance of 15 cm. The other end of the tube was connected to the gas analyzer. Ambient air was drawn by a scroll vacuum pump (XDS35i, BOC Edwards, UK) into the multi-pass absorption cell at a nominal flow rate of 14 L min−1. The cell pressure was maintained at 30−31 Torr (40.0 − 41.3 hPa). With this flow set-up, the flow regime was nearly turbulent with a Reynolds number of 3200. To prevent contamination of the cell mirrors by air particulates, two membrane filters with pore size of 5 μm and 0.45 μm were added in the tube and were replaced every 2 − 3 days to keep a relatively steady flow rate. In order to avoid water vapor condensation, the sampling tube was wrapped with opaque thermal insulation material, which was heated around 5 °C above ambient temperature. Zero gas calibration was automatically implemented on the QCL analyzer every 2 h by releasing pure nitrogen (NO-free) into the sampling inlet. In order not to disturb the covariance calculation at 30 min intervals, this procedure was scheduled to be performed during the first minute of each cycle of zero calibration. At the beginning and the end of the experimental period, a standard gas of NO (20 ppbv) produced by a multi-gas calibrator (146i, Thermo Scientific, USA) was introduced to the gas analyzer. The reported NO concentrations were 4% lower than the standard gas value before the experimental period and 5% lower thereafter. Hence, span calibration was performed offline by multiply the NO concentration with 1.05 (also see Section 2.5.1). Since the wind velocity and gas concentration data were stored separately, the two datasets were merged prior to further data processing and analysis. However, we found irregular time drifts in the clocks of the two data acquisition systems. To reduce the uncertainties in lag time determination between the two datasets, the clocks of the gas analyzer and CR3000 were both manually synchronized to internet time every 2 − 3 days, so that the nominal time difference between the two clocks was retained below 1.5 s.

the time and amounts of fertilizer application and irrigation. Our measurements were carried out in the late period of the hot pepper season, i.e., two months after nitrogen fertilization. All plots were not irrigated during the measurement period. Normally, at the end of the hot pepper season the plants were broken up by machine and ploughed into the soil layer of 0 − 30 cm. In 2015, however, this operation was conducted on July 3, almost one month earlier than in normal year. This was caused by several rainstorm events and continuous high soil water contents in spring, which imposed serious negative influences on the growth and yield of the hot pepper. Before ploughing, the canopy height ranged from 0.2 to 0.4 m, being much lower than in an average year (0.4 − 0.8 m) due to bad weather in the seedling stage. After ploughing, the ground was bare and unplanted until the end of the measurement period. 2.2. Eddy covariance measurements The wind components and the sonic temperature were measured by a three-dimensional sonic anemometer (CSAT3, Campbell Scientific Inc., USA) at 1.7 m above ground. The data were stored in a data logger (CR3000, Campbell Scientific Inc., USA) at a sampling frequency of 10 Hz. The gas concentrations were measured by a QCL gas analyzer (QC-TILDAS-DUAL, ARI, USA), which was equipped with a 76-m multipass absorption cell and two continuous wave mid-infrared lasers emitting at wave number of ca. 2246.3 cm−1 and 1900.1 cm−1, respectively. The latter laser was designed to measure NO, and the former to measure N2O, carbon dioxide (CO2) and water vapor (H2O) simultaneously. Here, the measured CO2 data were used to help the determination of lag time between NO and wind data. The control of laser operation and real-time data processing were realized by a software (TDLWintel, ARI, USA) installed on the personal computer embedded in the instrument (McManus et al., 2010). The 10 Hz concentration data in molar mixing ratios (nmol mol−1 or ppbv) were saved in an internal hard drive of the computer. The instrument cabin had a dimension of 2 × 2 × 2 m3 and was 3

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measuring the response of the laser to a standard gas of NO that mixed with varying water vapor contents. This correction was accomplished online by the TDLWintel software. Fifth, lag times between the time series of NO concentration and vertical wind velocity were corrected using the maximum cross-covariance method (McMillen, 1988). However, this method produced unrealistic results of lag time in the case of poor signal-to-noise ratio (SNR) in the NO concentration measurements. To solve this problem, the lag time between the CO2 and wind data was determined using this approach for each half-hour period, and the result was used for the lag time correction of NO. This is because the CO2 measurements had a much better SNR than NO. Such a solution was recommended for closed-path EC flux measurements when the trace gas emission is weak (Nemitz et al., 2018). Sixth, block averaging (Lee et al., 2004) was applied to calculate the instantaneous fluctuation of vertical wind velocity (w', in m s − 1) and ′ _ corr , in nmol mol−1 or ppbv). NO mixing ratio (cNO Finally, the turbulent fluxes of NO (FNO, μg N m−2 h−1) were calculated following Eq. (2).

2.3. Chamber measurements NO fluxes were manually measured using the static chamber technique during the period of July 5 − 14, following the method described in Yao et al. (2015). Briefly, each chamber flux was calculated according to the linearity of two NO concentrations of the chamber headspace that measured at the beginning and the end of each chamber enclosure (approximately 30 min). Two gas samples (2 L) were collected using a pump (NMP830KNDC, KNF Neuberger, Inc., Germany) at flow rate of ~3 L min–1 from an opaque static chamber with size of 0.5 × 0.5 × 0.5 m3. The gas samples were stored in opaque air bags and were analyzed soon afterwards by using a chemiluminescent NOeNO2eNOx analyzer (42i, Thermo Environmental Instruments Inc., USA). Chamber measurements were conducted if the wind direction was in the range of 60−150° (Fig. 1a). To cover the spatial variability of NO emissions, we set a total of 16 spatial replicates in the four field plots (N1–N4) that adjacent to the EC mast, with 4 replicates being located in each plot. The average NO fluxes calculated from all the spatial replicates was used to compare with the EC fluxes. Besides, we made the chamber measurements in the time period of 09:00–10:00 local standard time (LST), when near-ground turbulence was developed. This would increase data availability of the comparison between the chamber and EC fluxes.

FNO = 3600 × 14 × 10−3

T0 P1 w′c ′NO _ corr Vgas T1 P0

(2)

In Eq. (2), the overbar indicates the flux averaging period of 30 min, Vgas refers to the molar volume of a gas (0.02241 m3 mol−1) at the standard conditions of air temperature (T0 = 273.15 K) and pressure (P0 = 1013 hPa), T1 and P1 indicate the air temperature (K) and pressure (hPa) at the field conditions, respectively, 14 is the molar mass of nitrogen atom (g mol−1) and 3600 is the number of second per hour. The above steps, except for the corrections of water vapor and lag time effects, were accomplished using the EddyPro software (version 6.2.0, LI-COR Biosciences, USA).

2.4. Auxiliary measurements Environmental conditions including air temperature, soil temperature (5 cm), precipitation and solar radiation were observed every halfhour by a microclimate station (WS3000, Beijing Techno Solutions, China). Soil volumetric water content (VWC) of the top layer (0 − 6 cm) was manually measured everyday using a portable probe (ML2x, Delta-T Devices, UK). Bulk density (0 − 6 cm) was measured by weighting the oven-dried intact soil cores (4 spatial replicates) that sampled by stainless steel cylinders (100 cm3). The VWC records were converted into water-filled pore space (WFPS, in%) using the following equation: WFPS = 100VWC/ [1 – (bulk density/2.65)], in which 2.65 was the theoretical density of mineral particle (g cm–3). The surface soil (0 − 20 cm) in the main footprint areas were sampled for analysis of nitrate and ammonium concentrations. The soil samples were extracted using 1 M potassium chloride solution as soon as they were collected. The extracts were stored at a temperature of −18 °C until they were analyzed by an automatic nitrogen analyzer (San++ Continuous Flow Analyzer, Skalar Analytical B.V., the Netherlands).

2.5.2. Spectral correction of nitric oxide turbulent fluxes Eddy covariance flux measurements using closed-path analyzers are subject to spectral attenuation in the low and high frequency ranges (Massman, 2000; Moore, 1986). To correct this systematic underestimation, the transfer function method introduced by Horst (1997) was used, in which the corrected fluxes (Fcorr) were calculated with the measured fluxes (Fmeas) and the attenuation factor (Fa, with values from 0 to 1).

Fcorr =

Fmeas Fa

(3)

The Fa can be formally presented as 2.5. Data processing

Fa =

2.5.1. Calculation of nitric oxide turbulent fluxes The following processing procedures were applied to calculate the turbulent NO flux of each averaging period of 30 min. First, spikes in the 10 Hz raw data were removed following Vickers and Mahrt (1997). Second, the double rotation method proposed by Kaimal and Finnigan (1994) was performed to reduce the mean vertical wind velocity to zero in each averaging period. Third, span calibration was performed offline by multiplying the raw NO concentration data with 1.05. Fourth, the dilution effect (Webb et al., 1980) and the absorption line broadening effect (Neftel et al., 2010) caused by water vapor in the sample air were together corrected according to Eq. (1).

c NO _ corr =

c NO 1 − c H2O (1 + b)

∞ ∫0 TFL (f )TFH (f )Co(f ) df , ∞ ∫0 Co(f ) df

(4)

where f is the natural frequency (Hz), TFL and TFH the co-spectral transfer functions (dimensionless) in the low- and high-frequency ranges, respectively, and Co the un-attenuated frequency-weighted cospectrum after normalized by covariance and multiplied by natural frequency. Here, the Co can be presented as CowTa, i.e., the normalized co-spectrum of the temperature (sensible heat) flux. This is based on the assumptions that the normalized co-spectrums of all scalars have the same form (Kaimal et al., 1972) and that the temperature measurement by sonic anemometer is of adequate accuracy and not affected by attenuation. The temperature co-spectrum was solved using the surface layer cospectral model given by Horst (1997). The TFL in association with block averaging was determined theoretically using the sixth equation in Rannik and Vesala (1999). As the closed-path EC system behaved as a first-order response sensor, the TFH can be described with Eq. (5), where f is the frequency and τr the first-order response time of the whole measuring system (Horst, 1997).

(1)

Here, cNO_corr and cNO denote the corrected and raw NO concentrations, respectively, cH2O is the concentration of water vapor, and b is the correction coefficient. The value of b was 1.78, determined by 4

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TFH =

1 1 + (2πfτr )2

related to rainfall events (Fig. 2b). Before field ploughing (July 3), the WFPS values ranged from 47 to 68%, with a mean of 58%. Following field ploughing, the WFPS decreased to 20−34% mainly due to an increase in the soil bulk density. As ammonium bicarbonate and compound fertilizer were applied two months before the start of EC measurements, soil ammonium concentration was rather low, ranging from 2.2 to 5.3 (mean: 3.6) mg N kg–1 dry soil (Fig. 2c). By contrast, the nitrate concentration was larger by nearly one order of magnitude, ranging from 32.1 to 49.8 (mean: 41.7) mg N kg–1 dry soil (Fig. 2c). The nitrate data was somehow noisy probably due to spatial heterogeneity.

(5)

The value of τr was retrieved experimentally by fitting the ratio of the measured NO co-spectrum to the temperature co-spectrum in the frequency range of f > 0.01 Hz (Mammarella et al., 2009). The response time is regarded as a measure of frequency performance of the EC system. Longer response time means larger high-frequency flux attenuation. 2.5.3. Quality control of nitric oxide turbulent fluxes The half-hourly turbulent NO fluxes were quality-controlled by the following criteria/procedures. First, fluxes during instrument maintenance and malfunction were discarded. Second, fluxes with wind directions of 60–150° and 210–360° were rejected due to heterogeneous upwind area (Fig. 1a). Third, fluxes were rejected if they were assigned with quality flag of 2 according to stationarity and integral turbulence tests (Mauder and Foken, 2004), or if the friction velocity was below 0.10 m s − 1. Fourth, fluxes were regarded as spikes and thus removed if they exceeded two times the standard deviation of the adjacent six values.

3.2. Nitric oxide concentrations and fluxes Fig. 3 shows the valid half-hourly mixing ratio, EC fluxes and chamber fluxes of NO during the whole measurement period. Fig. 4 shows the same data as those in Fig. 3c (the period following field ploughing) as well as the meteorological variables. Table 1 lists the statistics of these valid data as well as the numbers of rejected fluxes due to different quality control criteria. The data coverage of valid NO mixing ratio was 82% after data rejection related to power failures and instrumental maintenance. Almost 80% of the missing data occurred in June, during which power failure happened quite frequently due to thunderstorm weather. During the whole measurement period, the minimum, maximum, mean and median value of the half-hourly NO mixing ratios were 0.0, 10.1, 0.6 and 0.4 ppbv, respectively. More than 80% of the flux sources came from our experimental field with homogeneous surface according to footprint analysis (Horst and Weil, 1994). We finally obtained 181 negative fluxes and 1102 positive fluxes of good quality, corresponding to data coverage of 44%. The minimum, maximum, mean and median values of the half-hourly fluxes were –7.1, 61.4, 6.3 and 3.5 μg N m−2 h−1, respectively. Removal of flux spikes and outliers only contributed 1.9% to the removed fluxes. Another 29.4% of the removed data were caused by non-stationarity and weak turbulence. Power failure, instrumental maintenance and limited fetch length explained most (68.7%) of the missing data, indicating a potential of increase in data coverage in future studies because these problems could be solved or improved in practice. The largest peak of NO concentration was observed in the early morning of July 14, probably being the result of weak wind condition and high soil emissions. However, we did not find a significant difference in the observed NO concentrations between the periods of P1 (May 16–July 3) and P2 (July 3–14), which were characterized by distinct magnitudes of NO fluxes. This implied that the soil emission intensity was not always the key factor dominating NO concentration in the nearground atmosphere, which could also be supported by the diurnal pattern of NO concentration and turbulent fluxes shown in Figs. 3 and 4. A typical diurnal variation pattern of NO concentration was observed, with a peak usually appeared between 05:00 and 10:00 LST and much lower concentration during the rest time of the day (Figs. 3 and 4). The daily maximum normally appeared at dawn and then quickly disappeared. During late nighttime, in particular, weak wind and low O3 concentration (not measured) led to NO accumulation near the ground despite of weak NO emissions. After dawn, this process was gradually inhibited; the accumulated NO was removed or consumed due to turbulence development and increasing concentration of photochemically produced O3. These results suggest that atmospheric NO concentration close to ground is determined together by soil emission, turbulence strength and local atmospheric chemistry. A typical diurnal pattern of turbulent NO fluxes was observed, especially during the period following field ploughing (Fig. 4a). The flux peaks usually appeared at noon or early afternoon, which agreed with the peaks of soil temperature. We also found that both daily maximums of NO flux and soil temperature had an increasing trend from July 7 − 14, indicating that soil temperature dominated the soil NO emissions (Fig. 4a and b).

2.5.4. Estimation of random errors and flux detection limit Random errors in the turbulent NO fluxes include the components caused by stochastic nature of turbulence (δturb) and instrumental noises (δinstr). They reduce the confidence of individual fluxes but cannot be corrected (Lenschow and Kristensen, 1985). In this study, the total random error (δtotal) of each half-hourly NO flux was estimated following the Finkelstein and Sims (2001) approach, in which δtotal was calculated as the integration of the auto-covariance and cross-covariance functions of the vertical wind velocity and the scalar concentration in a time domain exceeding the timescale of the turbulence. We multiplied the results by a factor of 4, to give δtotal at the 95% confidence interval. Flux random errors caused by instrumental noises reflect the lowest flux that the instruments are able to measure, i.e., the flux detection limit of an EC system. In this study, the estimates of δinstr were calculated using the method introduced by Rannik et al. (2016).

δinstr =

4σw σc fm Tv

(6)

In Eq. (6), fm is the measurement frequency; Tv is the flux averaging period; σw represents the standard deviation of vertical wind speed; σc represents the 1σ random noise level in scalar concentration measurements; and the factor of 4 implies that the estimated δinstr is within the 95% confidence interval. Eq. (6) applies under the assumption that the random noise component from vertical wind velocity measurement is negligible relative to that from the scalar measurement. In this study, σc of each averaging period was estimated using both methods introduced by Werle et al. (1993) and Lenschow et al. (2000), in which the Allan variance and the auto-covariance function of the measured NO signals were analyzed, respectively. 3. Results and discussions 3.1. Environmental conditions During the measurement period, nearly 74% of the wind came from the direction sectors of 0 − 60° and 150−210° (Fig. 1). The daily mean air and soil temperatures ranged from 20.2 to 32.7 °C and from 21.8 to 31.9 °C, respectively (Fig. 2a). The solar radiation data was converted into daily cumulative values, which varied from 1.8 to 25.4 MJ m–2 d–1 (Fig. 2a). The total precipitation was 402.7 mm, with more than 70% of which occurring in June (Fig. 2b). Soil water content was closely 5

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Fig. 2. Solar radiation and daily mean air and soil (5 cm) temperature (a), soil (0 − 6 cm) water-filled pore space (WFPS) and daily cumulative precipitation (b), and nitrate and ammonium concentrations of the 0 − 20 cm soil (c) during the measurement period. The error bars in panel c indicate standard deviation from their means (three spatial replicates).

with a mean of 14.9 μg N m−2 h−1 (Figs. 3c and 4a). We compared these fluxes with the simultaneously measured EC fluxes and found that the EC fluxes were 55% larger on average (P < 0.05). The chamber method applied in this study is quite simple in field operation and flux calculation, as it only requires two concentration data of NO. However, this method is criticized for the application of linear regression model, which causes significant underestimation in the trace gas flux (e.g., Kroon et al., 2008). Yao et al. (2015) estimated this error and reported an average underestimation of 31% in the measured NO fluxes. Apparently, the EC and chamber fluxes become comparable if the above underestimation is taken into account. Despite the fact that both the EC and chamber fluxes were subject to errors due to photochemical reaction of NO, the cross-validation between the two methods imply that the QCL analyzer is able to measure reasonable NO fluxes.

Although rather weak and noisy fluxes were observed in May, a diurnal variation with a single-peak pattern was still visible on some days (Fig. 3a). In June, the turbulent NO fluxes were characterized by much more stochastic data points around the zero line, mainly because the actual fluxes were below the instrumental detection limit (5.6 μg N m−2 h−1; see Section 3.6). The weak emissions during this period was probably related to low ammonium availability and high soil water contents (Fig. 2b). Both Fig. 3 and Table 1 show that the NO fluxes in P2 were significantly higher (P < 0.01) than those in P1. The higher fluxes during P2 could be explained by the favorable conditions for microbial nitrification of ammonium that stimulated by the field ploughing as well as much lower soil water contents. A total of 8 NO fluxes were obtained from chamber measurements from July 5 − 14. The fluxes ranged from 5.5 to 31.8 μg N m−2 h−1, 6

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Fig. 3. Half-hourly means of mixing ratio, eddy covariance (EC) fluxes and chamber fluxes of nitric oxide (NO). The flux data were quality controlled, while the mixing ratio data were only filtered in the cases of power failures and instrumental maintenance. In panel c, the error bars of chamber fluxes show the standard deviation from the means; the downwards arrow indicates the time of field ploughing.

Fig. 4. Half-hourly means of mixing ratio, eddy covariance (EC) fluxes and chamber fluxes of nitric oxide (NO), and meteorological variables during the period following field ploughing. Data in panel a are the same as those in Fig. 3c. The downwards arrow indicates the time of field ploughing.

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In order to estimate the systematic error due to use of CO2 lag times for NO flux calculation, we selected the data during P2, which had much more reasonable NO lag time estimates (data not shown). Then we calculated the half-hourly NO fluxes using the CO2 and NO lag times, respectively, finding that the latter was 3% higher on average. Hence, a correction factor of 1.03 was implemented to the NO fluxes calculated using the CO2 lag times during P1 (as shown in Fig. 3). In this study, the customized instrumental design, assembling two lasers in one instrument, enabled CO2 as a high SNR “reference” gas for the determination of lag times during the low flux period. However, if such a “reference” gas is not available, a prescribed lag time calculated from the inlet volume and the flow rate is another option for the lag time correction. However, this method is of less preference, because it also produces biases due to the changes in air humidity, filter condition, pump performance and so on (Nemitz et al., 2018). Besides, when applying this method, the relative drift in the time-stamps of the wind and concentration data needs to be eliminated. Installing an online clock synchronization software on both data acquisition systems is a solution for this problem (e.g., Hörtnagl and Wohlfahrt, 2014; Gerdel et al., 2017).

Table 1 Statistics of half-hourly mixing ratios and turbulent fluxes of nitric oxide (NO), and numbers of rejected fluxes due to different quality control criteria as listed in Section 2.5.3.

NO mixing ratios (ppbv)

NO fluxes (μg N m−2 h−1)

Numbers of rejected fluxes

Min Max Mean Median Sample size Min Max Mean Median Sample size QC1 QC2 QC3 QC4 Total

P1

P2

P1+P2

0.0 5.7 0.5 0.4 1828 −7.1 32.3 3.0 2.2 942 501 476 407 26 1410

0.2 10.1 0.8 0.6 515 0.6 61.4 15.3⁎⁎ 13.7 341 12 108 62 5 187

0.0 10.1 0.6 0.4 2343 −7.1 61.4 6.3 3.5 1283 513 584 469 31 1597

P1 and P2 refer to the periods of May 16−July 3 and July 3 − 14, respectively, being divided by field ploughing occurring on July 3, 2015. QC1−4 refer to quality control criteria due to power failures and instrumental maintenance, limited length of fetch, non-stationarity and weak turbulent development, and rejection of spikes and outliers, respectively. ⁎⁎ indicates a significant difference between P1 and P2 at P < 0.01.

3.4. Spectral characteristic Spectral analysis was done to assess the frequency performance of the instruments. Fig. 6 shows the ensemble averaged spectra and cospectra selected from P2 (i.e., after ploughing), according to following criteria: stratification was unstable; wind speed was in the range of 1.5 − 4.0 m s − 1; flux data was assigned with quality flag of 0; NO flux was larger than 10.0 μg N m−2 h−1 and absolute CO2 flux was larger than 0.02 mg C m−2 s − 1. In Fig. 6, to compare with the theoretical spectral behavior in the inertial subrange, natural frequency f is converted to normalized frequency fn = f(z − d)/U, where f, z, d and U denote natural frequency (Hz), measurement height (m), displacement height (m) and wind speed (m s − 1), respectively. As shown in Fig. 6a, the temperature and CO2 spectra agree each other. They both exhibit a theoretical slope of −2/3 in the inertial subrange (Kaimal et al., 1972) and only increased at the high-frequency end, being the characteristic of noise contribution. However, the NO spectra were dominated by white noise almost over the entire frequency range, indicated by slope of 1 (Kaimal and Finnigan, 1994). Being different from the NO spectra, the NO co-spectra were not affected by the noise and show a clear variation pattern (Fig. 6b). This is because white noise did not correlate with vertical wind velocity, but only introduced more random errors to the co-spectra. Both the NO and CO2 co-spectra agree with the temperature ones in the frequency range of fn < 1. However, they started to deviate from the temperature cospectra at the end (fn > 1), indicating obvious signal attenuation by the measurement system (Fig. 6b). With the above co-spectra, the TFH of NO and CO2 were calculated, and the corresponding response times were 0.13 s and 0.14 s, respectively. The same method was applied to the period P1 (i.e., before ploughing). However, it was not applicable due to large random errors in the calculated NO co-spectra. Since NO data during P1 were synchronized to wind data by using the CO2 lag times, theoretically the NO response time did not differ from that of CO2. This is proved by the similar co-spectra of NO and CO2 (Fig. 6b) during P2, as well as the similar NO and CO2 response times. Therefore, the transfer function of CO2 was used to correct for the high-frequency loss of NO during P1. In practice, we divided P1 into three sub-periods (May 16−31, June 1 − 16 and June 17−July 3) and performed the analysis respectively, assuming that τr of the measuring system was constant within each subperiod. The co-spectra used for the analysis were selected according to the same criterion that used for plotting Fig. 6. Results show that the response times estimated from the CO2 data ranged from 0.12 s to 0.15 s for the three sub-periods. Finally, the attenuation factor Fa was estimated at 0.89 on average

3.3. Lag times Determination of lag time for closed-path systems is quite challenging, particularly when fluxes are lower than instrumental detection limit. In this study, we found the maximum cross-covariance method was not suitable to obtain reliable lag time estimates of NO during the period P1, because this method requires gas measurement with higher SNR to present a clear peak in the cross-covariance function. Therefore, we used the CO2 data simultaneously measured by the second laser of the QCL analyzer, so as to correct the lag time between the NO concentration and vertical wind velocity during P1. Fig. 5 shows the NO and CO2 lag times estimated using the maximum cross-covariance method of an example period during P1 (May 18−23); the NO fluxes calculated using these NO and CO2 lag times were also presented. The CO2 lag times varied between −1.0 and 1.9 s, which combined the residence time of gas in the tube and time difference between the two data acquisition systems. The trend was clear and continuous except for the jump in the afternoon of May 19, due to manual operation of clock synchronization between the gas analyzer and CR3000. After time synchronization we found slightly larger lag times (1.8 − 1.9 s) than the nominal values (1.4 s) estimated from the flow rate and tube dimension. The difference could be explained by the unaccounted volumes of the analyzer's sample cell, the analyzer's response time and the spatial separation between anemometer and gas inlet. In the morning of May 20, the CO2 lag times started to decrease and varied within a range of 0 − 1.5 s, indicating that the clock of the gas analyzer was drifting. As shown in Fig. 5a, the NO lag times were fully random points scattering within the time window. With the inappropriate lag time estimates, the corresponding NO fluxes displayed an unnatural distribution around the zero line, switching between positive and negative values of similar magnitude (Fig. 5b). This “mirroring” pattern of flux data was also found in Langford et al. (2015). In this study, this phenomenon existed throughout the whole period of P1. Therefore, the CO2 lag times were adopted for NO flux calculation, which led to a more reasonable distribution of flux data (Fig. 5c). Most negative data points shown in Fig. 5b moved above or close to the zero line in Fig. 5c. However, the field ploughing on July 3 stimulated the NO emissions (Fig. 3c) and increased the SNR during P2, allowing us to obtain reliable NO lag times by applying the maximum cross-covariance approach. 8

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Fig. 5. Lag times and turbulent fluxes of nitric oxide (NO) during a selected period in May 2015. In panel a, the circles and diamonds represent the lag times derived by the maximum crosscovariance method using the concentration data of NO and carbon dioxide (CO2), respectively. The half-hourly NO fluxes presented in panels b and c were calculated with the NO and CO2 lag times showed in panel a, respectively. These fluxes were raw results without spectral corrections and quality control.

during the whole measurement period, meaning that the raw flux data were multiplied by a mean correction factor of 1.12. The factors in daytime were slightly larger than those in nighttime (1.13 and 1.11 on average, respectively). The fluxes shown in Fig. 3 are the results after this spectral correction.

3.5. Performance of nitric oxide analyzer The Allan variance technique is a graphical data analysis approach for examining the low-frequency component of a time series (Werle et al., 1993). It was used in this study to evaluate the precision

Fig. 6. Frequency-weighed normalized spectra and co-spectra of nitric oxide (NO), carbon dioxide (CO2) and air temperature (Ta). The frequency is normalized to dimensionless form fn = f(z − d)/U, where f, z, d and U denote natural frequency (Hz), measurement height (m), displacement height (m) and wind speed (m s − 1), respectively. Downwards straight lines in the spectra and co-spectra plots indicate the ideal slope of −2/3 and −4/3 in the inertial subrange, respectively (Kaimal et al., 1972). In panel a, the upwards straight line with slope of 1 is a sign of white noise (Kaimal and Finnigan, 1994). Spectra and co-spectra shown in the figure are the ensemble averages of those selected from the period P2 (i.e., after ploughing) according to following criteria: stratification was unstable; wind speed was in the range of 1.5 − 4.0 m s − 1; flux data was assigned with quality flag of 0; NO flux was larger than 10.0 μg N m−2 h−1 and absolute CO2 flux was larger than 0.02 mg C m−2 s − 1. 9

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Fig. 7. Time series of 10 Hz nitric oxide (NO) mixing ratio (dots) measured under field conditions during a 4-h period and the corresponding Allan variance (black solid line) as a function of integration time. The narrow gaps in the time series indicate the missing data during automatic zero-gas calibration every two hours. The downwards solid gray line represents the theoretical behavior of white noise, with the gray dashed lines showing the 95% confidential interval.

had a significant increase (P < 0.01) as compared to the first episode and showed an average value of 0.373 ppbv. We inferred this was the result of less frequent of instrumental maintenance during this episode. From July 4 to July 14, the instrument was running continuously and the σc_W decreased to 0.331 ppbv on average after optimization of optical alignment on July 4. The above results showed that the laser performance was stable as the noise level varied by ± 15% from the average value during the whole measurement period. The random instrumental noise was also estimated using the method of Lenschow et al. (2000), in which the noise (σc_L) was calculated as the difference between the signal auto-covariance at zero shift (i.e., the variance of NO time series) and the auto-covariance close to zero shift ( ± 0.05 s), with the latter being linearly extrapolated from the first five data points (10 Hz sampling frequency) of the auto-covariance function (Lenschow et al., 2000; Mauder et al., 2013). As shown in Fig. 8, the variation pattern and the magnitude of σc_W and σc_L agreed quite well. Table 2 lists the statistics of the instrumental noises upon the periods of P1 and P2. Although the flux magnitude during P2 were larger in general (P < 0.01) than those in P1 (Fig. 3), the instrumental noises did not show significant difference. Rannik et al. (2016) concluded that the Lenschow et al. (2000) method produced σc estimates with small bias for flux measurements with SNR < 3 or when closed-path gas analyzers were used (signal was attenuated). This was proven in our study by the identical magnitudes of σc_W and σc_L (Table 2). However, the time series of σc_L showed a much higher variability as compared to σc_W (Fig. 8). We inferred that this was caused by the low SNR measurements of NO, which had increased the random uncertainties in the extrapolated auto-covariance values. Hence, we would say that both methods have similar performance and give comparable estimates of instrumental noise in daily or longer averages, while the Werle et al. (1993) method is more suitable for short time (e.g., half-hour) evaluation. During the two-month measurement period, the QCL analyzer was working stably except for interruption due to power failure. This emphasize the importance of stable power supply, which is required to drive the QCL, the pump, the heating line on the sampling tube and the devices controlling the environmental temperature of the hut. From the

and stability of the QC laser for NO measurements. Normally, this approach is conducted with lab calibration using a standard gas to access possible instrumental drift. However, in order to examine instrument performance under field conditions, we performed this analysis using a piece of data (10 Hz NO concentration) collected from 19:00 to 23:00 on May 26, 2015 (Fig. 7). During this 4-h period, the atmospheric condition was near neutral, with an average wind speed of 1.9 m s–1; the calculated NO fluxes were close to zero, ranging from 1.4 to 5.7 μg N m−2 h−1 (Fig. 3a). As shown in Fig. 7, the time series of NO concentration displayed a flat trend with time and was dominated by highfrequency noises. The Allan variance decreased with a slope of –1 up to an integration time of about 100 s, showing that the data was dominated by white noise. Then, external noise started to appear and the variance tended to level off between 100 s and 2000s, with a minimum found at around 1000s. At longer integration time (> 2000s), the variance began to increase with a slope of 1 due to low-frequency drift. The plot pattern and the time domain that white noise signal dominated were quite similar to those obtained in laboratory conditions by the manufacturer. The stable performance of the laser under field conditions can be explained by placement of the instrument in a cabin with stable thermal condition. The signal drift at longer integration time was probably caused by non-stationarity of the atmospheric turbulence. The root square of the Allan variance provided a measure of the instrumental noise or measurement precision. In the example of Fig. 7, we calculated the precision of the NO analyzer to 0.266 ppbv and 0.084 ppbv at the integration time of 0.1 and 1 s (corresponding to the sampling frequency of 10 and 1 Hz), respectively. Following this method, we obtained the half-hourly estimates of the instrumental noise (σc_W) during the whole measurement period (Fig. 8a). The σc_W at 10 Hz sampling frequency was 0.339 ppbv on average, with a standard deviation of 0.039 ppbv (Table 2). This mean value was significantly identical to the specified value (0.316 ppbv) provided by the manufacturer. Meanwhile, we found the variation of σc_W was characterized by three episodes with distinct noise levels. The σc_W of the first episode (May 16−29) showed a decreasing trend and an averaging value of 0.281 ppbv, indicating slight improvement in laser performance with time. However, during the second episode (May 30−July 3), the σc_W 10

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Fig. 8. Instrumental noises (σc, i.e., instrumental precision at the integration time of 0.1 s) of the nitric oxide concentration measurements for each 30-min period that estimated following (a) Werle et al. (1993) and (b) Lenschow et al. (2000), respectively.

long-term measurements of NO fluxes under field conditions.

Table 2 Statistics of instrumental noise of nitric oxide (NO) concentration measurements and random errors in half-hourly turbulent NO fluxes. Period

P1

P2

P1+P2

σc_W

0.341 ± 0.044 (1828) 0.339 ± 0.046 (1828) 5.7 ± 3.2 (1828) 6.9 ± 3.0 (942) 386% ± 396% (942)

0.331 ± 0.005 (515) 0.331 ± 0.012 (515) 5.3 ± 2.4 (515) 8.1 ± 3.3 (341) 76% ± 72% (341)

0.339 ± 0.039 (2343) 0.337 ± 0.041 (2343) 5.6 ± 3.0 (2343) 7.2 ± 3.1 (128,377) 290% ± 364% (1283)

σc_L δinstr δtotal |Δδtotal|

3.6. Random errors and flux detection limit As Table 2 shows, δtotal and δinstr of the half-hourly NO fluxes during P1 and P2 did not show statistically significant difference, even though the fluxes during P2 were significantly larger than those during P1. During the whole measurement period, the δtotal of the valid halfhourly fluxes ranged from 0.7 to 24.2 μg N m−2 h−1, being 7.2 μg N m−2 h−1 on average (Table 2). The relative random error |Δδtotal|, i.e., the ratio of δtotal to the corresponding flux, were 386% and 76% (95% confidence interval) on average during P1 and P2, respectively. Soil ploughing occurred on July 3 enhanced the NO emissions and resulted in much lower |Δδtotal| during P2. Due to diurnal cycle in the NO fluxes, the |Δδtotal| values in daytime tended to be lower than those in nighttime, although the difference was not statistically significant. The δinstr of each flux averaging period was calculated according to Eq. (6) using the mean value of the instrumental noises derived by Werle et al. (1993) and Lenschow et al. (2000). The average value of δinstr was 5.6 μg N m−2 h−1 during the entire measurement period (Table 2). The δinstr dominated the δtotal, accounting for 85%, 68% and 80% during P1, P2 and the whole measurement period, respectively. We also calculated the δinstr with another widely applied approach that uses the standard deviation of cross-covariance function with time lags (200−400 s) far away from the true one (Wienhold et al., 1995). The results, however, not shown here, were systematically lower. This is probably because this method underestimates the true flux uncertainty as cross-covariance estimates at neighboring lags were not independent (Rannik et al., 2016). As mentioned earlier, δinstr can be considered as the flux detection limit of the EC system. We found that 37% of the valid half-hourly fluxes were larger than the mean estimate of δinstr, while the other 63%, with nearly one fifth being negative, were in the range of ± δinstr. That

Definitions of P1 and P2 were given in the footnotes of Table 1. σc_W and σc_L refer to the instrumental noises (nmol mol–1 or ppbv, at the integration time of 0.1 s) of NO concentration measurements for each 30-min period that estimated following Werle et al. (1993) and Lenschow et al. (2000), respectively. δinstr and δtotal refer to instrumental and total random errors in each half-hourly turbulent NO flux (μg N m−2 h−1), respectively. |Δδtotal| denotes the relative random errors (%). All values are presented in a format of mean ± standard deviation (number of samples). Each mean value in the table is given at the 95% confidence interval.

long-term measurement perspective, performance of the QCL relies heavily on instrumental maintenance. Here, we list the maintenance items of the QCL-based NO analyzer. Automatic zero calibration is needed every 2 − 4 h to avoid signal drift. Inlet filters need to be replaced at daily to weekly timescale. In this study we used two disposable membrane filters (5 μm and 0.45 μm) that are cheap and easy to replace. The optical alignment may become imperfect due to slight changes in cell temperature, it is suggested to adjust the laser and mirrors every week to month to optimize the optical alignment. At monthly to annual timescale, possible maintenance includes span calibration, replacement of Teflon gaskets inside the scroll pump, refilling of reference cell, cleaning of cell mirror, and so on. In practice, the items listed above are feasible, enabling the QCL analyzer to run for 11

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Table 3 Means of instrumental noise (σc, in nmol mol−1 or ppbv), flux detection limit (δinstr, in μg N m−2 h−1) and turbulent flux of nitric oxide (NO) (μg N m−2 h−1), as well as other measurement information of this study and three recent eddy covariance studies on NO fluxes. NO analyzera

σcb

δinstrc

NO flux

Ecosystem

Period

Remarksd

CLD, EP CLD, custom-built CLD, EP QCL, ARI

0.070 0.092 0.095e 0.338

1.0 0.9 1.0 − 2.0e 5.6

10.1 − 12.2 0.9e 2.5 − 5.0e 6.2

Forest Forest Cropland Cropland

10 days 1.5 months 7 months 2 months

[1] [2] [3] [4]

a

CLD, chemiluminescent detector. EP, EcoPhysics (model: CLD 780TR). QCL, quantum cascade laser. ARI, Aerodyne Research Institute (model: QC-TILDASDUAL). b One standard deviation at the integration time of 0.1 s. c At the 95% confidence interval. d References: [1] Rummel et al. (2002); [2] Min et al. (2014); [3] Vuolo et al. (2017); [4] this study. e Adapted from the references.

3.7. Systematic errors

means, for the present study, the EC system could only resolve part of the turbulent NO fluxes with high confidence at the 30-min timescale. NO emissions from common croplands are characterized by significant and intensive positive fluxes during the first several weeks after nitrogen fertilizer application and then low-level fluxes (usually 0 − 10 μg N m−2 h−1) during the rest of the time (e.g., Mei et al., 2009; Vuolo et al., 2017; Zheng et al., 2003). This was also the case at our experimental site, as the fertilizers were applied two months earlier than the flux measurements, and the observed turbulent NO fluxes were very low (median: 3.5 μg N m−2 h−1). Therefore, this closed-path EC system may not be the best tool to resolve the low fluxes at the halfhourly timescale. Nevertheless, the precision of this system was sufficient enough if daily or longer averages are of interest, as the flux detection limit would decrease by an order of n , wherein n is the number of half-hourly fluxes being taken for averages. As shown in Table 3, in the recent three EC studies on NO fluxes (Rummel et al., 2002; Min et al., 2014; Vuolo et al., 2017), closed-path CLDs were used and showed comparable instrumental random noises, which were much lower than that of the laser instrument used in this study. We found the flux detection limits were basically proportional to the instrumental noises, with the disagreement being caused by different estimation methods used (Lenschow and Kristensen, 1985; Wienhold et al., 1995; Rannik et al., 2016) and different levels of vertical wind velocity among these studies. In comparison, the flux detection limit estimated in this study was 2 − 5 times higher than those of the CLD-based EC systems (Table 3). Although the QCL-based EC system was not as good as the CLDbased ones in terms of measurement precision (Table 3), it had advantages in other respects. One is that the QCL analyzers are faster in response, because they measure gas concentration based on fast (1 − 5 kHz) laser frequency sweep and spectral fitting (McManus et al., 2010). It means that QCLs are capable of detecting NO concentration of independent air samples with time resolution of 10 Hz or even higher, provided that exchange rate of sample gas in cell is fast enough (not the case for some reactive gasses, e.g., NH3). The other advantage is that the QCL absorption spectroscopy technique minimizes the need for calibration. This had been proven in this study, wherein we detected very little span drift of NO signals from the beginning to the end of the experimental period (see Section 2.2). On the contrary, the CLD instruments need very frequent zero and span calibration to overcome instrumental drift. For example, Stella et al. (2013) and Vuolo et al. (2017) mentioned that they calibrated their CLDs every day and every 30 min, respectively. Therefore, although the closed-path dual-QCL gas analyzer cannot be an alternative to the chemiluminescent analyzers, it is still an effective option for long-term EC measurements of turbulent NO fluxes with the advantages of (ⅰ) stable performance, (ⅱ) high precision and fast response, and (ⅲ) feasible instrumental maintenance for long-term field measurements.

The measured NO fluxes were biased by systematic errors due to limited fetch length, weak turbulence in nighttime, high-frequency loss, and application of CO2 lag times for NO flux calculation during P1. As reported above, however, the first and second terms were already excluded by applying flux quality control; the other two terms had been corrected, being estimated to be 12% and 3%, respectively, relative to the raw uncorrected fluxes. Nevertheless, the measured turbulent fluxes were still underestimated as compared with the real soil NO emissions due to the chemical reaction loss of NO during transportation from soil surface to the gas sampling inlet. Vuolo et al. (2017) used the method developed by Duyzer et al. (1995) to correct this loss and found a difference of up to 30% between the fluxes at the soil surface and at a measurement height of 3.2 m. This correction method is based on the assumption of a logarithmic profile of the flux divergence and comparison between chemical and transport timescales of the NOeNO2eO3 triad, thus needs simultaneous data of stability function, friction velocity, concentrations and fluxes of NO, NO2 and O3 at the measurement height. Rinne et al. (2007), although studying on volatile organic carbon fluxes, introduced a stochastic Lagrangian transport model for the correction of chemical degradation below the sensor height. Similarly, application of this model also relies on simultaneous measurements of the variables in the NOeNO2eO3 triad. Regardless of the systematic errors due to NO loss through chemical reactions, this study only focused on the instrumental performance of the closed-path dualQCL gas analyzer and the EC system in measuring turbulent NO fluxes at the sensor height. 4. Conclusions A closed-path dual quantum cascade laser (QCL) gas analyzer was applied for measuring turbulent fluxes of nitric oxide (NO) using the eddy covariance (EC) technique. The measurements were conducted during a two-month summer period with the lowest NO emission intensity of the year, so as to evaluate the instrumental performance and applicability in measuring the turbulent fluxes of NO over a subtropical vegetable field. The instruments ran stably under the field conditions, with data losses only due to power failures in conjunction with frequent rainstorms. The field measurement precision of NO concentration was close to that obtained in laboratory and that provided by the manufacturer. This study particularly highlights the importance of placing the QCL gas analyzer in an environment with stable thermal conditions. Although power failures, instrumental maintenance and limited fetch length accounted for most of the missing data, these problems could be solved or improved in practice, implicating the potential to increase the data coverage of valid NO fluxes in future studies. The low signal-to-noise ratio (SNR) in NO concentration measurements largely biased the lag time determination as well as the high12

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frequency loss correction. Instead, however, using the lag times and high-frequency transfer function of the simultaneously measured carbon dioxide (CO2) was a good solution of both problems. Therefore, to measure low-level NO fluxes from croplands using an EC system based on a closed-path QCL absorption spectrometer, it is advisable to choose a gas analyzer capable of simultaneously measuring another gas species (e.g., CO2) with a high SNR. Due to the low emission intensity and large flux random errors, in this cropland case the EC system could only resolve a portion of the half-hourly turbulent fluxes of NO with high confidence. Nevertheless, it could be still qualified for measuring turbulent fluxes of NO over common croplands if the fluxes averaged at the daily or larger timescales are of interest. Also, the EC system was able to measure reasonable turbulent NO fluxes, which are comparable in magnitude with those observed by the static chamber method. In conclusion, the closed-path dual-QCL analyzer is still an effective option for long-term EC measurements of turbulent NO fluxes, even though it cannot be an alternative to the chemiluminescent analyzers. It shows the advantages of (i) stable performance, (ii) high precision and fast response, and (iii) feasible instrumental maintenance for long-term field measurements.

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Funding This study was jointly funded by the Ministry of Science and Technology of China (grant nos.: 2016YFA0602302 and 2012CB417100) and the National Natural Science Foundation of China (grant nos.: 41775141, 41405137 and 41975169). Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgments We thank Stanley Huang from Aerodyne for his technical assistance during the experiment. We would express our gratitude to the local farmer Mr. Huimin Zhou for his generous help in setting up the flux tower and the daily instrumental maintenance. We are grateful to Xiaoming Feng, Lin Wang and Rui Wang from IAP-CAS for their helps in analyzing the soil samples. Finally, we would thank Klaus ButterbachBahl and the two anonymous reviewers for their valuable suggestions that allow us to improve the paper. References Ammann, C., V., Wolff, O., Marx, C., Brümmer, Neftel, A., 2012. Measuring the biosphereatmosphere exchange of total reactive nitrogen by eddy covariance. Biogeosciences 9, 4247–4261. Baldocchi, D.D., 2003. Assessing the eddy covariance technique for evaluating carbon dioxide exchange rates of ecosystems: past, present and future. Glob. Change Biol. 9, 479–492. Bollmann, A., Conrad, R., 1998. Influence of O2 availability on NO and N2O release by nitrification and denitrification in soils. Global Change Biol. 4, 387–396. Butterbach-Bahl, K., Gasche, R., Breuer, L., Papen, H., 1997. Fluxes of NO and N2O from temperate forest soils: impact of forest type, N deposition and of liming on the NO and N2O emissions. Nutr. Cycl. Agroecos. 48, 79–90. Butterbach-Bahl, K., Kahl, M., Mykhayliv, L., Werner, C., Kiese, R., Li, C., 2009. A European wide inventory of soil NO emissions using the biogeochemical models DNDC/ forest DNDC. Atmos. Environ. 43, 1392–1402. Civerolo, K.L., Dickerson, R.R., 1998. Nitric oxide soil emissions from tilled and untilled cornfields. Agric. For. Meteorol. 90, 307–311. De Arellano, J.V.G., Duynkerke, P.G., 1992. Influence of chemistry on the flux-gradient relationships for the NO-O3-NO2 system. Boundary-Layer Meteorol. 61, 375–387. Delany, A.C., Fitzjarrald, D.R., Lenschow, D.H., Pearson, R., Wendel, G.J., Woodruff, B., 1986. Direct measurement of nitrogen oxides and ozone fluxes over grassland. J. Atmos. Chem. 4, 429–444. Dunfield, P.F., Knowles, R., 1999. Nitrogen monoxide production and consumption in an organic soil. Biol. Fert. Soil. 30, 153–159.

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