Influence of deployment time and surface wind speed on the accuracy of measurements of greenhouse gas fluxes using a closed chamber method under low surface wind speed

ABSTRACT

As a convenient method, the closed chamber method has been applied to determine gaseous emission fluxes from fully open animal feeding operations despite the measured fluxes being theoretically affected by deployment time, wind speed over the emitting surface and detected gas mass. This laboratory study evaluated the effects of deployment time (0 to 120 min) and external surface wind speed (ESWS) (0.00, 0.25, 0.50, 0.75, 1.00, 1.50 and 2.00 m sec^-1) on the measurement accuracy of a 300 mm (diameter) × 400 mm (height) (D300×H400) closed chamber using methane (CH₄), nitrous oxide (N₂O) and sulfur hexafluoride (SF₆) as reference gases. The results showed that the overall deviation ratio between the measured and reference CH₄ fluxes ranged from 9.99 % to -37.32 % and the flux was overestimated in the first 20 min. The measured N₂O and SF₆ emissions were smaller than the reference fluxes using the chamber. N₂O measurement accuracy decreased from -14.47 to -35.09% with deployment time extended to 120 min, while SF₆ accuracy sharply increased in the first 40 min, with the deviation stabilizing at approximately -5.00%. CH₄, N₂O and SF₆ measurements were significantly affected by deployment time and ESWS (P<0.05), and the interaction of those two factors greatly influenced CH₄ and SF₆ measurements (P<0.05). With the D300×H400 closed chamber, deployment times of 20 to 30 min and 10 to 20 min are recommended to measure CH₄ and N₂O, respectively, from the open operations of dairy farms under wind speeds lower than 2 m sec^-1.

Implications: This study recommended the suitable deployment times and wind speeds for using a D300 × H400 closed chamber to measure CH₄, N₂O, and SF₆ in an open system, such as a dairy open lot and manure stockpile, to help researchers and other related industry workers get accurate data for gas emission rate. Deployment times of 20 to 30 min and 10 to 20 min were recommended to measure CH₄ and N₂O emissions using the D300 × H400 closed chamber, respectively, from the open operations of dairy farms under wind speeds lower than 2 m sec⁻¹. For the measurement of SF₆, a typical tracer gas, a deployment of 70 to 90 min was suggested.

Introduction

Open lots are ancillary operations commonly used for keeping animals outside the barns, especially for dairy cows in China, where the bedding area, water tank, and open space for free activities are typically provided. Open dairy lots are important emission source of contaminant gases, including ammonia (NH₃) and greenhouse gases (GHGs), to the atmosphere because of the animals and manure accumulation on it. It is still challenging to directly measure the emissions from lots, due to their fully open characteristic, relatively low gas flux, and interference with other emissions sources such as barns and cows (Ding et al. 2016a; 2016b).

Several techniques, including flux chambers, tracer gas, and micrometeorological methods, have been applied to quantify NH₃ and GHGs emissions from open sources (Laubach et al. 2013; Lovanh, Warren, and Sistani 2010; McGinn et al. 2011; Misselbrook et al. 2001; Spiehs et al. 2012). Tracer gas and micrometeorological methods are considered ideal techniques; however, these methods either require expensive equipment (Laubach et al. 2013; McGinn et al. 2011) or are typically influenced by the emission sources, including wind profile, and by the detection errors of low emission gases (Harper, Denmead, and Freney 1999). Under these conditions, the closed chamber method is a good alternative for field detection because it is easier, smaller, and cheaper (Denmead 2008).

The closed chamber method estimates gas flux based on the accumulated concentrations of target gases by inserting the closed chamber into emission surface within a certain period (Ding et al. 2015) and is widely used to monitor gaseous emissions between the earth’s surface and the atmosphere (Livingston, Hutchinson, and Spartalian 2006), for soils in particular. Closed chamber measurements are documented as being impacted by their own configurations and settings (de Klein and Harvey 2013; Ding et al. 2015; Pihlatie et al. 2013; Pumpanen et al. 2004), the microclimate of the covered emitting surface (Davidson et al. 2002; Reichman and Rolston 2002), measured molecular gas (Davidson et al. 2002; Wan et al. 2005; Yao et al. 2009), and deployment time (Davidson et al. 2002; Wan et al. 2005), as well as sampling interval and location (de Klein and Harvey 2013).

Theoretically, wind speed and deployment time can change the mass emissions process by altering concentration and pressure gradient between both sides of the emitting surface. Previous studies reported that water evaporation rates increased with wind speed increasing over soil columns (Acharya and Prihar 1969; Benoit and Kirkham 1963). Kimball and Lemon (1971) documented that wind speeds from 0 to 5 m sec⁻¹ positively affected heptane evaporation from soil. However, because most of these studies are carried out for soils, there is little information on using closed chambers on livestock farms, which are regarded as important GHGs sources.

The main GHGs emissions from dairy open lots are CO₂, CH₄, and NO₂. Suitable deployment times for these gas emission measurements from soil sources reported in previous studies varied greatly (de Klein and Harvey 2013; Livingston, Hutchinson, and Spartalian 2006; Petersen et al. 2012; Tanka, Poul, and Lars 2016; Wan et al. 2005). Wan et al. (2005) recommended that deployment times for CO₂, N₂O, and CH₄ measurements should be within 25 min, 15–30 min, and less than 30 min, respectively. At the same time, other studies have suggested capturing small fluxes of CO₂, N₂O, and CH₄ with a relatively longer deployment time of 30–60 min (de Klein and Harvey 2013; Livingston, Hutchinson, and Spartalian 2006; Petersen et al. 2012; Tanka, Poul, and Lars 2016). Obviously, there is no explicit deployment time for detecting these gases with different molecular masses, for livestock operation emission measurements in particular, and it is essential to understand the effect of deployment time and to confirm the special operations for gases of different molecular masses using a certain chamber technique.

To scientifically operate a closed chamber to quantify GHGs emissions from an open operations of dairy farms, a simulation system was set up in the laboratory to (1) evaluate the influences of external surface wind speed (ESWS) and deployment time on measurement accuracy for different GHGs emissions; (2) find appropriate deployment time for chamber operations; and (3) recommend appropriate operational approach of the chamber on open operations of dairy farms.

Materials and methods

Experimental design

Ding et al. (2016a; 2016b) found that up to 92.97% of the average surface wind speeds over open lots and manure stockpile in a commercial dairy farm from spring to winter in Beijing were within 0–2 m sec⁻¹. Thus, seven expected ESWS gradients within that range were designed in this study, namely, 0.00 m sec⁻¹, 0.25 m sec⁻¹, 0.50 m sec⁻¹, 0.75 m sec⁻¹, 1.00 m sec⁻¹, 1.50 m sec⁻¹, and 2.00 m sec⁻¹. Five replicate measurements of each gradient were conducted.

To assess the impacts of deployment time on the measurement accuracy of the closed chamber for reference gases, a gradient ranging from 0 to 120 min with a 10-min interval was designed, covering nearly all of the studied deployment time before. Five replicates were tested for each deployment time.

Experimental setup

Tests were carried out at the Key Laboratory of Agricultural Engineering in Structure and Environment, Ministry of Agriculture and Rural Affairs, Beijing, China. A wind tunnel was employed to offer expected airflows at the emitting surface to simulate wind effect in practice (Figure 1a). The closed chamber system was placed 1 m away from the air outlet of the wind tunnel, where a steady wind profile, including wind speed and direction, could be generated in the test area (Figure 1a).

Figure 1. Schematic diagram of the experimental setup in the laboratory. All units are in meters. (a) Top view of wind tunnel and testing area. (b) Closed chamber and gas sampling systems. ×, T and RH are sensors to measure air temperature and relative humidity of the test area; ∆, hot wire anemometer to measure wind speed over the emitting surface of the closed chamber headspace.

Wind tunnel

The wind tunnel was designed based on the criteria of ANSI/AMCA Standard 210–99 and GB20068418-T326 and its structure was given by Li et al. (2006). Controlled by a frequency adaptor, the rated power, maximum airflow rate, and adjustable rotating rates of the fan in the wind tunnel were 37 kW, 60,000 m³ hr⁻¹, and 30–150 rpm, respectively.

Closed chamber and calibration system

A cylindrical closed chamber with a size of 300 mm (diameter) × 400 mm (height) (D300× H400), made of acrylic, was designed to evaluate the impacts of ESWS and deployment time on measurement accuracy. The closed chamber consisted of a cylindrical body, a cover, and an air circulation system (Figure 1b). The air circulation system was set for gas mixing in the headspace. A gas monitor was connected to the air circulation system by Teflon tubes (3 mm inner diameter) to continuously monitor gas concentrations. Two ports on the cover served as air inlet and outlet (Figure 1b). The air sampling point was located in the middle of the body, and the air outlet port was in the cover. The gap between the cylindrical body and its cover was sealed with a rubber gasket.

A chamber calibration system, designed based on Widén and Lindroth’s study (2003), was constructed to offer an emission source with known reference gas fluxes. The system was made up of a reference gas resource, a cylindrical diffusion tank with a lid, a turbulence fan, and a gas analyzer. A detailed description was given by Ding et al. (2015). The lid was full of quartz sand with its aeration porosity of 35% and bulk density of 2.2 g cm⁻³ measured using the Archimedean method, to simulate the agricultural fields of gas emissions (Grünzweig et al. 2003). In China, unpaved soil is mostly used as the floor base for open dairy lots. The bulk density of unpaved soil is 2.1 g cm⁻³, and its porosity is about 28% to 32%, which are similar to those of quartz sand. In this study, 0.2% CH₄, 0.05% N₂O, and 0.5% SF₆ sources, with molecular masses of 16.04 g mol⁻¹, 44.01 g mol⁻¹, and 146.05 g mol⁻¹, respectively, were released into the calibration system as target gases. The reference flux in this system was assumed as a constant and steady supply because of the very small concentration decreased over the deployment time (Pihlatie et al. 2013).

Measurement of gas concentration, wind speed, temperature and relative humidity

As shown in Figure 1b, wind speed over the emitting surface was recorded at a 10-min interval by a hot-wire anemometer (model KA41 L, Kanomax, Osaka, Japan), which was located 5 cm above the operating platform. The test was not started until the wind speed was under a steady state of the targeted value for at least 15 min. Ambient temperature (T) and relative humidity (RH) in the test area were automatically monitored with three T and RH sensors (Testo175-H1, FLEXIM, Berlin, Germany), which were located in an equilateral triangle pattern on the platform (in Figure 1a). The sampling interval was 30 min.

In order to reduce the effect of edge turbulence, the closed chamber was inserted into the quartz sand at a depth of 50 mm, 24 hr prior to the first measurement. Before starting a test, a certain amount of targeted gas was injected into the diffusion tank through the valve to reach an initial concentration of approximately 90 mg m⁻³ and then completely mixed by the turbulence fan set on the bottom of the tank. The emission fluxes of CH₄, N₂O, and SF₆ from quartz sand under ESWS of 0 m sec⁻¹ were 2.93 ± 0.43 mg m⁻² hr⁻¹, 2.52 ± 0.28 mg m⁻² hr⁻¹, and 2.00 ± 0.19 mg m⁻² hr⁻¹, which were similar to the average CH₄ and N₂O emissions from open dairy lots of 0.85–12.41 mg m⁻² hr⁻¹ and 1.48–2.40 mg m⁻² hr⁻¹ (Ding et al. 2016b). The flow was measured by a digital flowmeter (LZB-3 WB, Changzhou Shuanghuan Metal Products Co., Ltd, Changzhou city, China), keeping each gas flux down to 100 L min⁻¹. After the reference gas in the diffusion tank had been released into the quartz sand for approximately 2 hr to achieve a stabilization state, testing began. Reference gas concentrations of CH₄, N₂O, and SF₆ in the closed chamber and diffusion tank were continuously measured and automatically stored by a photoacoustic multigas monitor (model: INNOVA 1412i, Lumasense Technologies A/S, Santa Clara, CA; range: 0.4–4000 ppm for CH₄, 0.03–300 ppm for N₂O, 0.006–50 ppm for SF₆; accuracy: ±12 ppb for CH₄, ±15 ppb for N₂O, ±10 ppb for SF₆) at every 10-min interval, combined with a multiplexer. The sampling flux of the photoacoustic multigas monitor with a built-in pump was 4 L min⁻¹, and the sampling time was set up to 1.5 min. During sampling, target gases from the headspace were continuously extracted into the middle sampling point by the gas monitor, and were circulated into the closed chamber at the same time through the air outlet port on the cover to improve the gas mixing in the headspace. During the sampling, the surface wind speed inside the chamber headspace was measured at 0.13 ± 0.04 m sec⁻¹. Ding et al. (2015) reported that the measured fluxes by a D300× H400 closed chamber were no statistical difference under the inner surface wind speed of 0.00 m sec⁻¹ and 0.45 m sec⁻¹. Hence, the influence of the air flow inside the headspace caused by the monitor when sampling was ignored.

Gas emission estimation

Estimation method has a great influence on accurately assessing the emission flux. Gao and Yates (1998) reported that when the chamber height was 40 cm, gas concentrations in the chamber showed linear increase within 90 min. In our prestudy, accumulated concentrations versus deployment times fit linear regressions well under different ESWS at the longest deployment time (120 min) for CH₄, N₂O, and SF₆, with R² values all greater than 0.97. Thus, a linear calculation scheme was used to estimate flux. The gas flux from the emitting surface, measured with the closed chamber method, was calculated by Butterbach-Bahl, Kiese, and Liu (2011):

(1) ${\mathrm{F}}_1=\frac{\mathrm{V}}{\mathrm{A}}\cdot \frac{{\mathrm{d}}_{\mathrm{c}}}{{\mathrm{d}}_{\mathrm{t}}}$(1)

where F₁ is the gas flux measured by the closed chamber (mg m⁻² min⁻¹); V is volume in the headspace of the chamber (m³); A is the emitting surface area covered by the chamber (m²); d_c is the accumulated gas concentration in the chamber headspace during a certain time (mg m⁻³); and d_t is a certain time of chamber enclosure (min).

The reference gas flux of the calibration system, considered as the “true” emissions rate (reference flux) from the emitting surface, was estimated by Pumpanen et al. (2004):

(2) $\begin{array}{rl}& {\mathrm{F}}_2=\\ & \frac{{\mathrm{V}}_2\left({\mathrm{C}}_{\mathrm{f}}\left({\mathrm{t}}_1\right)-{\mathrm{C}}_{\mathrm{f}}\left({\mathrm{t}}_2\right)\right)+{\mathrm{V}}_{\mathrm{s}}\left({\mathrm{C}}_{\mathrm{f}}\left({\mathrm{t}}_1\right)+{\mathrm{C}}_{\mathrm{a}\mathrm{m}\mathrm{b}}\left({\mathrm{t}}_1\right)\right)/2-\left({\mathrm{C}}_{\mathrm{f}}\left({\mathrm{t}}_2\right)+{\mathrm{C}}_{\mathrm{a}\mathrm{m}\mathrm{b}}\left({\mathrm{t}}_2\right)\right)/2}{\left({\mathrm{t}}_2-{\mathrm{t}}_1\right){\mathrm{A}}_2}\end{array}$(2)

where F₂ is the reference flux of the target gas (mg m⁻² min⁻¹), and V₂ and V_s are the volumes of the diffusion tank and the quartz sand pore layer (m³), respectively. The volume of sand pore layer is equal to the volume of the lid by the sand porosity of 35%; C_f(t₁) and C_amb(t₁) are the target gas concentrations in the diffusion tank and ambient atmosphere at t₁ (mg m⁻³), respectively; C_f(t₂) and C_amb(t₂) are the target gas concentrations in the diffusion tank and ambient atmosphere at t₂ (mg m⁻³), respectively; t₁ and t₂ are the times of the test starting and finishing (min), respectively; and A₂ is the emitting surface area of the diffusion tank (m²).

Assuming C_amb was constant during the whole experiment, this equation can be rearranged as

(3) ${\mathrm{F}}_2=\frac{2{\mathrm{V}}_2+{\mathrm{V}}_{\mathrm{s}}}{2{\mathrm{A}}_2}\cdot \frac{\mathrm{d}{\mathrm{C}}_{\mathrm{f}}}{{\mathrm{d}}_{\mathrm{t}}}$(3)

Then, a deviation ratio (DR) between the measured flux with the closed chamber method and the “truth” emission rate (reference flux) can be described as

(4) $\mathrm{D}\mathrm{R}=\frac{{\mathrm{F}}_1-{\mathrm{F}}_2}{{\mathrm{F}}_2}\cdot 100\mathrm{\%}$(4)

Statistical analyses

When calculating the emission flux of the closed chamber, the maximum and minimum values of the five replications were excluded. Results were expressed as “means ± standard deviation” (with a significance level of 0.05), and SPSS (version 21.0) was used to do statistical analyses. Least significant differences, repeated measurement analyses of variance (ANOVAs), and correlation analyses were done to analyze the influences of deployment time and ESWS on the measurement accuracies of the closed chamber method.

Results and discussion

Performance of closed chamber on CH₄, N₂O, and SF₆ measurements

Table 1 shows the overall average deviation ratios between the measured flux with the closed chamber and the reference flux of the target gases under different deployment times. The results indicate that the deviations between detected and reference CH₄, N₂O, and SF₆ fluxes were from 9.99 to −37.32%, −14.47 to −35.09%, and −4.21 to −27.55%, respectively, during the whole deployment period. The molecular masses of CH₄ and N₂O are comparatively light, so they could mix rapidly into the closed chamber during a relative short enclosure period to reduce the concentration difference between the emission surface and the chamber. As a macromolecule gas, SF₆ might not be completely mixed at different heights of the closed chamber for a relative short enclosure period, which might be part of the reason that the measurement accuracy was the lowest at the deployment period of 20 min. Moreover, a slight SF₆ concentration difference was observed in different heights inside the closed chamber in the pre-experiment, which suggested more time needed to mix the gas released into the chamber without turbulence.

Table 1. Deviation ratios (%) between measured and reference fluxes under different deployment times.

	Deployment time, min
Target gas	10	20	30	40	50	60	70	80	90	100	110	120
CH4	9.99 ± 5.30^a	2.00 ± 2.45^b	−3.39 ± 2.43^c	−9.74 ± 2.62^d	−15.64 ± 1.86^e	−20.38 ± 3.68^f	−23.23 ± 3.82^fg	−27.07 ± 3.64^g	−29.79 ± 3.62^gh	−32.37 ± 3.09^h	−34.94 ± 3.14^hi	−37.32 ± 3.00^i
N2O	−14.47 ± 2.82^a	−15.26 ± 2.92^a	−18.13 ± 2.83^b	−20.08 ± 3.56^bc	−21.71 ± 3.08^c	−23.64 ± 2.28^cd	−25.65 ± 1.64^d	−27.53 ± 1.28^de	−29.33 ± 1.30^e	−31.32 ± 0.89^ef	−33.27 ± 0.91^f	−35.09 ± 0.92^fg
SF6	−27.55 ± 11.26^a	−19.77 ± 4.85^b	−13.98 ± 3.73^c	−8.57 ± 3.63^d	−7.03 ± 2.79^d	−5.49 ± 2.76^d	−4.21 ± 2.53^d	−4.40 ± 2.14^d	−4.45 ± 2.52^d	−5.09 ± 1.76^d	−5.68 ± 1.69^d	−6.31 ± 2.01^d

Notes. Different lowercase letters of each row indicate significant differences at the 0.05 level.

Significant differences were found among CH₄ deviation ratios for each of the closed periods in the first 1 hr (p < 0.05), with values changing from 9.99% to −20.38%. The deviations at 20 min and 30 min were comparatively close to 0%, suggesting that those might be appropriate deployment times. Weishampel and Kolka (2008), as well as Wan et al. (2005), also recommended a deployment time of 30 min to measure CH₄ with the chamber technique. No significant difference was observed between N₂O deviations at 10 min and 20 min (p > 0.05), and the ratios were −14.47% and −15.26%, respectively. At the same time, the measurements at these two deployments were significantly different from the others (p < 0.05). This suggests that a comparatively good time to measure N₂O with a D300× H400 closed chamber without a vent or fan is 10 to 20 min, which is similar to the recommendations of Philippe and Nikita (2008) and Wan et al. (2005). Generally, SF₆ measurement accuracies at 10, 20, and 30 min were significantly lower than at other times (p < 0.05), and there were no significant differences among measurements from 40 to 120 min (p > 0.05), with deviation ratios from −4.21% to −8.57%. Additionally, SF₆ deviation ratios were all less than −5% at the 70 to 90 min deployment times, which were recommended in this paper. Our finding is longer than the 55 min recommendation of Ding et al. (2015) for measuring low emission rates by the chamber technique, while the deviation was approximately −15% in Ding et al. (2015), a value much bigger than we found in this study.

Influence of deployment time on measurement accuracy of closed chamber

Deployment time is an important factor on the operation performances of certain chambers. Covered by a closed chamber, the microenvironment of the measured emitting surface and chamber headspace can change over deployment time (Gao and Yates 1998; Reichman and Rolston 2002), which may include factors such as surface wind speed, temperature, humidity, and the pressure gradient, increasing the uncertainty of the chamber measurements.

Figure 2 shows CH₄, N₂O, and SF₆ measurement accuracies using the closed chamber method at varied deployment times and ESWS. Deviation ratios of the measured and reference fluxes of the three gases showed the same trend under different wind speeds with extended closed time (Figure 2), which illustrated that ESWS values of less than 2.0 m sec⁻¹ did not dramatically impact gas emissions under the closed chamber. At the same time, measurement was remarkably affected by the vertical emission of gases over time, which agreed with previous findings (Gao and Yates 1998; Reichman and Rolston 2002). As shown in Figure 2a, CH₄ flux measured by the closed chamber method was overestimated in the first 20 min, and then underestimation occurred when the deployment was extended over 30 min. With a closed lid, the atmospheric mixing processes near the emission surface were instantaneously affected (Hutchinson et al. 2000), and the feedback would increase the volume-to-area (V/A) ratio of the chamber, causing an overestimation in a short enclosure period (Rachhpal et al. 2012). N₂O and SF₆ fluxes measured by the closed chamber method were all smaller than the reference fluxes during the test, which was consistent with the findings of Rochette and Bertrand (2007). For N₂O, measurement accuracy decreased with increased deployment time, and the deviation ratios were lower than 20% in the first 20 min. SF₆ deviation decreased in the first 40 min, and then it trended toward stabilization with measurements extending to 120 min. It could be seen that the post-lid-closure feedback had a bigger impact on lighter gases.

Figure 2. Deviations among CH₄, N₂O, and SF₆ fluxes measured with the closed chamber method, and reference fluxes of target gases under different external surface wind speeds (ESWS) as well as different deployment times: (a) CH₄, (b) N₂O, and (c) SF_6.

As a key factor on emission measurements from an open source, deployment period is also documented to be dependent on chamber configuration, including the V/A ratio and the vent and fan settings. Table 2 shows some study findings, suggesting similar deployment times of chambers with different configurations and settings. Installation of a vent and fan in a closed chamber may also affect the inner environment, which may consequently impact the measurement accuracy of the chamber. In the case of Weishampel and Kolka (2008), the V/A ratio of the chamber was smallest, while a vent helped to balance the gas concentration gradient between the inner chamber and atmosphere, hence relatively extending deployment time. Wan et al. (2005) applied a much larger V/A ratio and illustrated that the setting of the fan in the chamber headspace shortened the closed time because of quicker, better blending. Air cycling by a built-in pump of the INNOVA analyzer used in this study may also serve a fan function to some extent.

Table 2. Examples of suitable deployment times for measuring GHG emissions in closed chambers with different configurations and settings.

References	V/A ratio, cm	Vent	Fan	Emission surface	Deployment time, min
					CH4	N2O	CO2	SF6
Weishampel and Kolka 2008	3–7.5	Yes	No	Wetland	30	——	——	——
Parker et al. 2017	12.2	Yes	Yes	Beef cattle feedlot manure and soil	——	20	——	——
Wan et al. 2005	100	No	Yes	Dry cropland	<30	15–30	<25	——
Kutzbach et al. 2007	5,15	Yes	Air cycling by pump	Wet polygonal tundra	——	——	8, 10	——
Ding et al. 2015	30	Yes	Yes	Quartz sand	——	——	——	55
This study	35	No	Air cycling by INNOVA	Quartz sand	20–30	10–20	——	70–90

Effect of external surface wind speed (ESWS) on measurement accuracy of closed chamber

During the study, ESWS of the test setup was close to the designed value and is shown in Table 3.

Table 3. Fan frequency and average wind speed over the measurement surface provided by the wind tunnel during the test.

Frequency of fan, Hz	0	3	5	7	8	13	16
Wind speed, m sec^−1	0.01 ± 0.01	0.26 ± 0.04	0.51 ± 0.05	0.73 ± 0.05	1.00 ± 0.03	1.50 ± 0.03	2.06 ± 0.06

Figure 3 shows the deviation ratios of CH₄, N₂O, and SF₆ under different ESWSs, in which the curves of the three gases reflected the influence of ESWS on measurement with deployment times of 20 to 30 min, 10 to 20 min, and 70 to 90 min, respectively. The results indicated that the deviation ratios were from 2.21 to −3.02% and from −12.74 to −17.17% for CH₄ and N₂O, respectively; there were no significant differences on the measurements under varied ESWS from 0 to 2 m sec⁻¹ (p ˃ 0.05) during a relatively short deployment time. Redeker, Baird, and Teh (2015) reported that CH₄ flux was underestimated using the closed chamber technique, and significant differences on deviation ratio were observed with the ESWS increasing from 0.00 to 2.00 m sec⁻¹. However, forest, grassland, and peat bog were explored as emission surfaces in their study, and were usually more porous than quartz sand. For example, the porosity in the humus layer could exceed 80% (Pihlatie et al. 2013). Gas fluxes from these porous surfaces are more easily positively affected by wind turbulence (Widén and Lindroth 2003), which hence increases the CH₄ flux underestimation beneath the chamber as the ESWS increasing. Reicosky and Denmead (2003) found large differences in CO₂ loss between tilled and nontilled soil, and stated higher air permeability of the soil was more sensitive to wind turbulence, which caused higher gas exchange. Thus, porous emission surface might be the reason leading to greater underestimation. Meanwhile, the soil-water content in these natural emission surfaces may also enhance the phenomenon. The soluble gas flux, such as CO₂, N₂O, and H₂S, might be underestimated even more with the increasing of humidity in manure (Lund et al. 1999). According to Philippe and Nikita (2008), the confidence level of the CH₄ measurement using a D300× H400 closed chamber in our study was high (<10%) and that of N₂O was at a medium confidence level (<30%). The influence of ESWS on SF₆ measurement differed from that of CH₄ and N₂O; the underestimation ratios under different wind speeds were 2.39, 4.04, 4.83, 8.28, 2.77, 6.17, and 1.99%, respectively, which were all within a high confidence level (<10%) (Philippe and Nikita 2008).

Figure 3. Deviations in measurement accuracies of CH₄, N₂O, and SF₆ vs. ESWS outside the closed chamber with deployment times of 20 to 30 min, 10 to 20 min, and 70 to 90 min, respectively.

Interaction between deployment time and ESWS

The correlation analysis, given in Table 4, shows there was no correlation between ESWS (under 2.06 m sec⁻¹) and measurement accuracies of the closed chamber for CH₄, N₂O, or SF₆ (p > 0.05), while all three gases were significantly influenced by deployment time. Deployment negatively affected CH₄ and N₂O measurement accuracies (p < 0.05), and it was positively correlated with SF₆ measurement (p < 0.05) because more time was needed for mixing after being released into the chamber headspace, illustrating that varied deployment times should be considered when measuring different molecular gases. Data in Figures 2 and 3 also indicate that the influence of deployment time played a more important role for chamber measurements than ESWS, mainly because the chamber cut off the lateral flux of gases beneath the emitting surface, and the measurement accuracy was generally affected by vertical emission. The deviations in SF₆ for all tested ESWS had no significant differences (p > 0.05) with a deployment time of 40 min, except for the wind speeds of 0.51 m sec⁻¹ and 1.00 m sec⁻¹. This result differed from that of another deployment time. It indicated that deployment time and wind speed may have a complex interaction on the measurement accuracy of the closed chamber.

Table 4. Correlation analysis of deployment time and ESWS for CH₄, N₂O, and SF₆ measurement accuracies as detected in a closed chamber.

Factors	CH4	N2O	SF6
Deployment time	−0.960**	−0.953**	0.675**
ESWS	0.008	−0.081	0.047

Note. ** Significant difference at 0.01 level.

The main effect analysis listed in Table 5 indicates influences of deployment time, ESWS, and the interaction of the two factors on the measurement accuracies of CH₄, N₂O, and SF₆, where the ranges of deployment time and ESWS were 0 to 120 min and 0.00 to 2.06 m sec⁻¹, respectively. This shows that all three gases were clearly affected by deployment time and ESWS (p < 0.05) and that the interaction of the two factors significantly affected CH₄ and SF₆ measurement accuracies (p < 0.05), except for N₂O (p > 0.05).

Table 5. Main effect analysis of deployment time and ESWS and the interaction of the two factors for CH₄, N₂O, and SF₆ measurement accuracies detected in a closed chamber.

	CH4		N2O		SF6
Factors	F	Significance	F	Significance	F	Significance
Deployment time	681.91	0.00	94.92	0.00	142.96	0.00
ESWS	22.63	0.00	9.47	0.00	32.70	0.00
Deployment time × ESWS	2.99	0.00	0.84	0.79	4.50	0.00

Notes. R²_{(accuracy of CH4)} = 0.979, R²_{(accuracy of N2O)} = 0.873, R²_{(accuracy of SF6)} = 0.925.

Figure 2 shows the influences of deployment time on different gases and illustrates the importance of finding a suitable time for measuring gas emission fluxes. With a suitable deployment period, the CH₄ and N₂O measurement accuracies were not affected by ESWS (Figure 3). When deployment time was extended or shortened, this phenomenon changed. For N₂O, with a time from 30 to 60 min, the ESWS had a significant influence, especially with wind speeds of 1.50 m sec⁻¹ and 2.06 m sec⁻¹, which dramatically decreased accuracy (p < 0.05). At ESWS of 1.50 m sec⁻¹ and 2.06 m sec⁻¹, the Reynolds numbers were 4530 and 6040, respectively, suggesting that turbulent flow promoted gas emissions out of the chamber, making accuracy worse.

Operation of closed chamber to measure GHG emissions from dairy production

In China, open lots are typical open dairy operations housing dairy cattle, where the manure is commonly scraped out, and the collected solid manure is temporally stacked outside uncovered from weeks to months. The accumulation and penetration of manure on the lots as well as the manure stockpile are important sources of GHGs and NH₃ emissions to the atmosphere, especially for the CH₄ and N₂O. As reported, the annual CH₄ and N₂O emissions from open dairy lots with diurnal and seasonal variations are of 0.85–757.27 mg m⁻² hr⁻¹ and 0.15–27.2 mg m⁻² hr⁻¹ (Ding et al. 2016b; Leytem et al. 2011), respectively. Because of its open characteristic, direct measurement of the emission is still challenging, especially for the comparatively low flux rate. Pihlatie et al. (2013) proved the possibility of measuring low emission flux levels from 0.20 to 2.30 mg m⁻² hr⁻¹ using CH₄ as the target gas by the closed chamber technique, and showed the mean chamber fluxes to reference fluxes ratios were not significantly different among the four fluxes (p = 0.99). In this study, the tested flux rates of the three target gases were 2.93 ± 0.43 mg m⁻² hr⁻¹, 2.52 ± 0.28 mg m⁻² hr⁻¹, and 2.00 ± 0.19 mg m⁻² hr⁻¹ for CH₄, N₂O, and SF₆, respectively, close to the high level that Pihlatie et al. (2013) explored.

Based on the results of this study, deployment times of 20 to 30 min and 10 to 20 min were recommended to measure CH₄ and N₂O emissions using the D300× H400 closed chamber, respectively, from the open operations of dairy farms under wind speeds lower than 2 m sec⁻¹. For the measurement of SF₆, a typical tracer gas, a deployment of 70 to 90 min was suggested. This recommendation satisfied the configuration suggested by de Klein and Harvey (2013), which specified that the chamber height to deployment time ratio and insertion depth to deployment time ratio should be not less than 40 cm hr⁻¹ and 12 cm hr⁻¹, respectively. In practice, aluminum foil wrapping for the whole chamber is suggested to avoid the effect of solar radiation when operating an open field measurement. To avoid air leakage of the headspace, the chamber should be cut into the manure or sealed 24 hr prior to the test. When used on dairy farms, the chamber should be guarded or protected away from the animals.

The quartz sand used in this study was a homogeneous resource, which was differed from most natural emission resources of dairy operations, including the open lots ground and the manure stockpile. Unpaved soil is a typical type for open dairy lots, and the bulk density and porosity are similar to quartz sand. However, dairy manure is a complex porous medium containing GHGs sources from both heterotrophic and autotrophic respiration, and is usually more porous and much wetter than quartz sand, as indicated by Ding et al. (2016b), so that the humidity in the fresh manure can exceed 84%. Compared to quartz sand, the turbulence out of the chamber has been proven to have a bigger influence on dairy manure because of its higher porosity, which then increases the underestimation of target gas measurement (Lai et al. 2012; Widén and Lindroth 2003). Furthermore, the target gas underneath the chamber, storied in the pores of manure, would help to increase the underestimation (Conen and Smith, 2000). However, increased water content of dairy manure in the opposite direction is expected to decrease the effects of positive pressure within the chamber (Lund et al. 1999) to diminish the underestimation. In reality, the interaction of both porosity and humidity differences of the emission surfaces on gas emission is more complicated, which may alter the measurement accuracy of the closed chamber. Also, the influence for the GHGs emission measurement on open dairy lots and manure stockpile using a closed chamber should be further verified by field tests.

Summary

The effects of deployment time and wind speed over the emitting surface on the measurement accuracies of a D300× H400 closed chamber were evaluated in the laboratory using CH₄, N₂O, and SF₆ as reference gases. CH₄ measurement was overestimated in the first 20 min by the closed chamber, and then underestimation occurred as the deployment time extended, while the N₂O and SF₆ fluxes were all underestimated under all tested runs. Deployment time negatively affected CH₄ and N₂O measurement accuracies, but positively affected that of SF₆. CH₄ and SF₆ measurement accuracies were also greatly influenced by the interaction of the deployment times and ESWS.

At surface wind speed under 2.00 m sec⁻¹, deployment times of 20 to 30 min and 10 to 20 min are recommended to measure CH₄ and N₂O emissions from open dairy lots and manure stockpile using the D300× H400 closed chamber, respectively. Measures to protect the closed chamber from solar radiation, air leakage and animal activities should be taken for a better accuracy when operating the field measurements.

Additional information

Funding

This study was funded by the National Nature Science Foundation of China (grant no. 31472132), the Scientific Fund of the University (no. 2015SYL001), and the China Agricultural Research System (CARS-36).

Notes on contributors

Yu Liu

Yu Liu is a Ph.D. student in the Department of Agricultural Structure and Bioenvironmental Engineering, China Agricultural University (CAU), China.

Chaoyuan Wang

Chaoyuan Wang is a professor in the Department of Agricultural Structure and Bioenvironmental Engineering, College of Water Resources and Civil Engineering, China Agricultural University, China.

Zixin Wang is an undergraduate student in the Department of Agricultural Structure and Bioenvironmental Engineering of CAU.

Luyu Ding

Luyu Ding is an assistant scientist in the National Engineering Research Center for Information Technology in Agriculture, China.

Zixin Wang

Zixin Wang is an undergraduate student in the Department of Agricultural Structure and Bioenvironmental Engineering of CAU.

Guanghui Teng

Guanghui Teng, Zhengxiang Shi, and Baoming Li are professors in the Department of Agricultural Structure and Bioenvironmental Engineering, College of Water Resources and Civil Engineering, China Agricultural University, China.

Zhengxiang Shi

Guanghui Teng, Zhengxiang Shi, and Baoming Li are professors in the Department of Agricultural Structure and Bioenvironmental Engineering, College of Water Resources and Civil Engineering, China Agricultural University, China.

Baoming Li

Guanghui Teng, Zhengxiang Shi, and Baoming Li are professors in the Department of Agricultural Structure and Bioenvironmental Engineering, College of Water Resources and Civil Engineering, China Agricultural University, China.