This article showcases the static chamber-based method for measurement of greenhouse gas flux from soil systems. With relatively modest infrastructure investments, measurements may be obtained from multiple treatments/locations and over timeframes ranging from hours to years.
Measurement of greenhouse gas (GHG) fluxes between the soil and the atmosphere, in both managed and unmanaged ecosystems, is critical to understanding the biogeochemical drivers of climate change and to the development and evaluation of GHG mitigation strategies based on modulation of landscape management practices. The static chamber-based method described here is based on trapping gases emitted from the soil surface within a chamber and collecting samples from the chamber headspace at regular intervals for analysis by gas chromatography. Change in gas concentration over time is used to calculate flux. This method can be utilized to measure landscape-based flux of carbon dioxide, nitrous oxide, and methane, and to estimate differences between treatments or explore system dynamics over seasons or years. Infrastructure requirements are modest, but a comprehensive experimental design is essential. This method is easily deployed in the field, conforms to established guidelines, and produces data suitable to large-scale GHG emissions studies.
Understanding the contributions of both human activities and natural systems to radiative properties of the atmosphere is an area of critical importance as we strive to mitigate anthropogenic contributions to the greenhouse effect. In addition to carbon dioxide (CO2), nitrous oxide (N2O) and methane (CH4) are also potent GHGs, accounting for an estimated 7% and 19% of global warming, respectively, with the majority of emissions coming from landscape sources1,2. These range from managed systems such as agricultural fields, rice paddies, and landfills, to natural systems such as forest floors, wetlands, and termite mounds. Accurate measurement, supporting well-informed modeling of such landscape-based emissions is critical in order to understand the drivers of climate change as well as to identify mitigation opportunities.
A variety of greenhouse gas measurement strategies exist, each with their own strengths and weaknesses2-5. Mass balance techniques rely on wind-based dispersion of gases and are suited to measurement of flux from small, well-defined sources such as landfills and animal paddocks. Micrometeorological approaches such as eddy covariance are based on real-time direct measurement of vertical gas flux, and can provide direct measurements over large areas. However, homogeneity in source topography is an implicit assumption (in that measurements yield a mean for the area under study), and costly infrastructure can limit deployment possibilities. Finally, chamber-based methods focus on change in gas concentration at the soil surface by sampling from a restricted above ground headspace. They allow measurements to be obtained from small areas and numerous treatments, but are subject to high coefficients of variation due to spatial variation in soil gas flux.
Here we discuss the most prevalent and easily implemented form of chamber-based measurement, utilizing the type of closed chambers without air flow-through commonly referred to as “static” or “non-steady-state non-flow-through” chambers. In this approach, gas emissions from the soil surface are trapped within a vented chamber, and rates of flux are determined by measuring the change in gas concentration over time within the chamber headspace. The static chamber technique has been widely deployed across both managed and natural landscapes and underpins the bulk of data reporting soil-based flux of greenhouse gases, particularly N2O6,7. It is ideally suited to the study of small experimental plots, diverse sites over variable terrain, or in other situations where multiple distinct locations must be studied without significant infrastructure investments. Typical experimental uses might include the exploration of alternative landscape management practices and their impact on soil-based CO2, N2O, and/or CH4 emissions, examination of landscape-based flux dynamics under artificially induced climate change scenarios such as warming and rainfall exclusion/supplementation, or the descriptive study of natural and agricultural ecosystems and subsystems.
As a critical tool in GHG measurement and flux estimation, the static chamber method has been thoroughly evaluated, and significant efforts have been made towards standardization of techniques and harmonization of data reporting4,6,8,9. Of particular note are the detailed reviews and guidelines produced by the U.S. Department of Agriculture – Agricultural Research Service’s Greenhouse gas Reduction through Agricultural Carbon Enhancement network (GRACEnet)8 and by the Global Research Alliance on Agricultural Greenhouse Gases (GRA)9. Such guidelines provide an invaluable resource and platform for coordination, as ultimately the interoperability of data from a myriad of studies is critical for scaling up local findings to global modeling, and for translating research results into viable mitigation strategies.
GRACEnet, GRA, and other reviews also highlight the fact that specific techniques in static chamber-based greenhouse gas flux measurement are extremely diverse, with significant methodological variations possible at nearly every step of the way, including chamber design, temporal and spatial deployment, sampling volumes, sample analysis, and flux calculations. The method described here presents one possible variant, while showcasing best practices and highlighting critical considerations for the generation of high quality, broadly transferrable data. It is intended to provide an accessible overview of this standardized procedure, and a platform from which to explore further nuances and variations described in the literature.
1. Chamber Construction and Anchor Installation
2. Calibration and Experimental Design
Note: Prior to beginning the experiment, follow these steps to determine an appropriate sampling time course that will allow data to be fit to an appropriate linear or non-linear flux model (see Parkin et al.12). This will require the use of techniques described in steps 3-5 (Field Sampling, Sample Analysis, and Data Analysis). Optimal timing is a function of both the system under study and the dimensions of chambers being used. Some trial and error may be involved. See Venterea13 for alternate approaches.
3. Field Sampling
Note: On each sampling date, follow the sampling scheme established in section 2.4, using the techniques described below. Equipment and sample volume can vary depending on the collection and transfer methods being employed and the amount of sample required for GC analysis8. This protocol utilizes 5.9 ml collection vials and 30 ml syringes, with a flushing method of sample transfer. See Discussion for alternate approaches.
4. Sample Analysis
PV = nRT
Where P = pressure, V = volume, n = moles of gas, R = gas law constant, and T = temperature. Thus:
5. Data Analysis
F = S • V • A-1
Where F = flux, S = slope of the regression, V = chamber volume, and A = chamber area. Thus:
Note: Refer to the Discussion and Parkin et al.12 for non-linear approaches to flux calculation.
Prior to beginning a research project with static chambers, it is important to understand the overall workflow, and the organization of in silico, field- and laboratory-based elements (Figure 1). Provided careful experimental design and system calibration (Figure 2), data analysis will generally be relatively straightforward. A rate of flux is determined for each chamber and sampling time by regression of time by concentration using a pre-determined flux model appropriate to the system (Figure 3). However, even following best practices, difficulties may be encountered, and quality control of raw data is critical. For example, failure of a chamber seal or leaky sample vials can result in anomalous concentration values. These are readily identified through visual inspection of time series concentration plots (Figure 4), with CO2 time series often serving as a particularly useful indicator due to the typically more robust and continuous flux of CO2 compared to sometimes negligible, near-detection-limit, or even negative fluxes of N2O or CH4. Once data quality has been confirmed, results may be used to compare gas flux dynamics between treatments or over the course of a season (Figure 5). As can be seen from May and June flux values and error bars, the variation caused by spatial heterogeneity of flux may be significant, and more pronounced under conditions producing high rates of flux. Such variability is not unusual, and underscores the importance of sufficient replication in this technique.
Figure 1. Workflow overview. Various elements of this protocol will be carried out in the planning stage, in the field, in the laboratory, and in silico. Arrows indicate the sequence of workflow, beginning with chamber design (and construction if necessary), and concluding with data analysis. Multiple boxes/arrows between field sampling and sample analysis represent the possibility of multiple sampling dates over the course of an experiment.
Figure 2. Sample timing. An example timing scheme for the collection of samples from multiple chambers simultaneously. Chamber numbers are indicated at left and time points at top, with sampling times listed in whole minutes within the grid. In this example, four separate time series of 36 min each (one for each chamber) are carried out within the space of 46 min, with 12 min spacing between time points within a series, and 2 min walking time between chambers. For this hypothetical example, the suitability of 36-min time series would have been determined by prior calibration. While evenly spaced timing is not necessary, it often simplifies the sampling scheme. Alternately, researchers may individually record each sampling timepoint to determine sampling intervals.
Figure 3. Flux calculation. A typical static chamber time series, consisting of N2O concentrations measured at four time points over the course of a 36-min sampling period. The linear regression is displayed, the slope of which yields flux rate.
Figure 4. Quality control. Paired time series from the same set of samples but different gases are shown in which vial leakage has been identified by visual inspection (red point). A) CO2 concentration over time. B) N2O concentration over time.
Figure 5. Synthesis results. N2O flux rate from an agricultural field over the course of a single growing season. Flux values represent the mean of six chambers, using four-point time series. Error bars are standard error.
The static chamber-based approach described here is an efficient method for measurement of GHG flux from soil systems. The relative simplicity of its components makes it especially well suited to conditions or systems in which more infrastructure-intensive methods are infeasible. In order to generate high quality data, however, the static chamber approach must be carried out with strict attention to experimental design6. One notable consideration that must be taken into account is the spatial variability of soil gas fluxes, which can result in high variability among replicate chamber-based measurements. In designing experiments, therefore, it is important to include enough replicates to provide adequate power for statistical analysis. Tradeoffs may exist between the number of treatments which can be studied while maintaining sufficient replication, and a minimum of four replicates per treatment is a general guideline14.
If measured fluxes will be used to estimate daily emissions, diurnal variations in air temperature, soil temperature, and gas emissions must be taken into account. If research goals require measurements to be obtained in mid-morning when temperatures reflect daily averages, the restricted window for sampling may affect the number of chambers that can feasibly be monitored. An additional consideration to be evaluated is the impact that inclusion or exclusion of plant roots and above ground biomass will have on gas fluxes. Chamber placement relative to plant tissue will impact the interpretation of flux data, particularly in the case of CO2 where not only microbial respiration but also root and shoot respiration and photosynthesis must be appropriately balanced. For additional discussion of these factors, see Parkin and Venterea8.
As noted previously, many variations on this methodology exist, including chamber design and sampling volume. One such variation is in the method employed to transfer samples between the syringe and collection vial. The technique described here first flushes the collection vial with sample before filling the vial to positive pressure5. A more commonly used technique is the transfer of samples from syringes to vials that have been pre-evacuated using a vacuum pump, and the use of non-evacuated vials without flushing has also been reported8,17. Another significant point where a range of approaches exists is in data analysis and the selection of the flux model most appropriate to the system under study. In addition to the linear regression method described here, non-linear models may also be employed, particularly when longer deployment times are used. These models include the algorithm developed by Hutchinson and Mosier18 and derivations thereof19,20, the quadratic procedure described by Wagner et al.21, and the non-steady-state diffusive flux estimator described by Livingston et al22. For a thorough discussion of non-linear flux models, refer to Parkin et al.12 and Venterea et al23.
Methods similar to the static chamber approach include the use of flow-through measurement systems with Fourier transfer infrared (FTIR) spectrometry as an alternate to syringe sampling and gas chromatography, as well as automation of chamber closure and sampling through various means. Automated systems enable more frequent measurements with reduced personnel, but also require additional infrastructure investments. Grace et al.24 provide an extensive summary of options and tradeoffs in automated chamber-based N2O measurement.
Characterization of greenhouse gas flux from both managed and natural systems is important to inform process-based models, understand the impacts of management practices and inform mitigation strategies, and to support global accounting and climate change modeling. Thus while individual studies are informative at the local scale, much additional value is derived through contributing to, and drawing from, a global body of knowledge on gas exchange between the landscape and the atmosphere. It is key, therefore, that data be collected and reported in a way that ensures longevity and interoperability with the broader knowledge base. This includes following best practices to ensure data quality, as well as collection of ancillary measures and comprehensive reporting of metadata to allow extension of findings beyond discrete studies. Excellent guidelines for data reporting are available from the GRACEnet project and the GRA25.
The authors have nothing to disclose.
This material is based upon work supported by the National Science Foundation under Grant Number 1215858, by the US Department of Agriculture under Grant Number 2013-68002-20525, and by the US Department of Energy Great Lakes Bioenergy Research Center – DOE BER Office of Science (DE-FC02-07ER64494) and DOE OBP Office of Energy Efficiency and Renewable Energy (DE-AC05-76RL01830). In-field video and images were recorded at the Wisconsin Integrated Cropping System Trial project of the University of Wisconsin–Madison. The authors are grateful to Ryan Curtin for skillful videography and editing.
5.9 ml soda glass flat bottom 55 x 15.5 mm | Labco Limited | 719W | Collection vials |
16.5 mm screw caps with pierceable rubber septum | Labco Limited | VC309 | Caps for vials |
90-well plastic vial rack, 17.1 mm well I.D. | Wheaton | 868810 | Rack for organizing vials |
Regular bevel needles 23G x 1" | BD | 305193 | Needles for sample collection |
Stopcocks with luer connections, 1-way, male slip | Cole-Parmer | EW-30600-01 | Stopcocks for syringes |
30 ml syringe, slip tip | BD | 309651 | Syringes for sample collection |
Stopwatch or timer | Various | N/A | For timing field sampling |
Stainless steel or galvanized utility pans with rim, or fabricated stainless steel or PVC chambers and lids, dimensions as appropriate to experimental system | Various | N/A | Chamber anchor and lid – bottom cut out of anchor, holes for septum and vent tubing bored in lid |
Gray butyl stoppers 20 mm | Wheaton | W224100-173 | Chamber septa for syringe sampling – insert into hole bored in lid top |
Tygon tubing 4.0 mm I.D. x 5.6 mm O.D. | Sigma-Aldrich | Z685623 | Chamber vent tubing – insert in hole bored in lid side, flush with exterior, approximately 25 cm coiled in lid interior (a 1ml syringe tip may be used as an attachement mechanism) |
Adhesive foam rubber tape or HDPE O-ring | Various | N/A | Chamber sealing mechanism – fastened to underside of lid rim |
Reflective insulation, 0.3125" thickness | Lowe's | 409818 | Insulating and reflective coating – affix to exterior of chamber lid |
Large metal binder clips, 2" size with 1" capacity, or manufactured draw latch as appropriate | Staples / McMaster | 831610 (Staples) / 1863A21 (McMaster) | Lid attachment mechanism – for clamping lid to anchor during sampling |
Gas chromatography equipment fitted with electron capture detector for nitrous oxide, infrared gas analyzer or thermal conductivity detector for carbon dioxide, flame ionization detector for methane | Various | N/A | For sample analysis |