Measurements of soil gas efflux are key to understanding carbon and nitrogen cycling, and have helped answer research questions in fields ranging from fundamental biology to ecosystem management. Manual “static” chamber measurements have been used extensively for several decades and these data have been critical in developing our understanding of nutrient cycling in many ecosystems; however, these manual methods also have their downfalls. Now that automated chamber systems are widely available and can be coupled to highly precise  analyzers, researchers have expanded options for gas emission measurements. This case study will go through some of the key considerations when deciding whether you should switch to an automated system, keep the manual approach or use a combined approach for measurement and monitoring of soil gas fluxes. 

Considerations for Switching

Consideration #1: Research Goals

Soil flux measurements can be broadly split into two categories: The first category includes studies where accurate absolute carbon or nitrogen balances are important, or where researchers are studying certain aspects of soil nutrient loss or plant physiology. The second category involves studies where relative flux measurements can answer the research questions (e.g.  when studying agricultural treatment effects or comparing fluxes from two contrasting ecosystem types). Chamber and analyzer requirements, including automation and accuracy tolerances, can be very different between these two research goal categories.

Consideration #2: Spatial and Temporal Coverage

Sites can be vastly different both in size, and environmental drivers that create temporal variability in gas fluxes. For large (acres and larger) field sites, automated chamber systems are often impractical because of the limitations placed on them by power requirements and maximum tubing and cable lengths. Similarly, for sites with high temporal variability manual chambers are likely to miss key flux events because measurements are taken based on personnel schedules rather than on timing and variability of gas flux drivers. A combined automated and manual approach is recommended by some researchers, but is probably only necessary for sites where both spatial and temporal dynamics must be accurately captured to achieve the research goals.

Consideration #3: Flux magnitudes

Depending on the expected flux magnitude, different measurement approaches may produce different results. Generally speaking, automated systems will provide a more precise and accurate measurement of the gas fluxes, however the sensitivity of these systems may be too high in environments where fluxes are very large (e.g. CO2 fluxes at volcanic sites) or where the fluxes are large enough that accuracy/precision is no longer a concern. This also applies to sites where relative measurements are required. For example, if the expected treatment effect (e.g. fertilizer application) is large enough, the additional precision afforded by the automated system may not be required. Conversely the limited number of gas samples taken with static chambers and the analytical precision of laboratory-based gas analysis may cause small or negative fluxes to be imperceptible and thus treated as null measurements, biasing overall emissions.

When to Go Automated

Reason #1: Improve Your Detection Limits

A recent study of tropical rainforest in Puerto Rico by O’Connell et al. found strong impacts of both slope and soil moisture conditions on the fluxes of carbon dioxide (CO2) and methane (CH4). The ability to capture these slope and moisture impacts relied heavily on the accuracy gained by using a laser-based analyzer and the sample size of the data set; over 40,000 automated measurements were collected over the course of 150 days (see Figure 1, and O’Connell et al. (2018), Nature).

Figure1. Soil carbon dioxide emissions across topographic zones and drought time periods.

In an earlier study, Christiansen et al. (2015) showed that flux measurements gathered using a laser-based analyzer system had a minimum detectable flux (MDF) 2.25 to 50 times lower than those collected using traditional Gas Chromatography- based (GC) measurements (2.5x for N2O, 35x for CO2 and 50x for CH4). This improvement in MDF was partly driven by increased detection accuracy for some gas species, but was largely driven by density  of measurements that can be collected during each chamber closure using laser-based systems. Enhanced MDF also ensures more chamber measurements meet or exceed QA/AC criteria, here 4x more measurements were significant when comparing the laser to the GC.

Reason #2: Minimize Data Biases

There are many factors that can bias flux measurements, including issues with gas analysis, chamber design, etc. However, one of the most avoidable sources of bias is personnel scheduling. Figure 2 shows fluxes measured by a researcher who collects measurements at 9:00 am each morning compared to a researcher who collects measurements at noon. These two researchers would draw very different conclusions about the gas flux dynamics at this site based on the data collected over the same 3 day period (read more about biases of temporally sparse data here).

The continuous dataset shown in Figure 3 reveals that neither researcher A or researcher B  is capturing the diel trends in flux at the site. This bias is more extreme at sites that have highly sporadic fluxes, caused by events like fertilizer application, rainfall or ebullition. It’s highly likely that events are being missed or improperly accounted for. In some instances, missing these events could bias annual flux estimates by more than half.

Figure 2. Fluxes measured at the same site by two researchers visiting the site 3 hours apart.
Figure 3. Comparison between continuous flux measurements and the fluxes observed via discrete sampling by the two researchers as shown in Figure 2. Air temperature at the site is also shown.

Reason #3: Save Time and Gain Focus

When manual sampling methods are used, researchers and lab staff devote a significant amount of their time to secondary work like running samples in the GC and then processing the resultant data. Using an automated system dramatically reduces the amount of time spent on secondary work as it largely eliminates the need for lab-based sample analysis and reduces data processing times (Figure 4, to learn more about automated vs manual data processing timelines, see this case study). This allows researchers to get to the heart of their research questions faster. As a result, an automated system also provides time to revisit the site to collect additional data. Alternatively, automated systems provide researchers with the ability to process the data on-site in real time, which means they can make site setup or measurement adjustments on the fly. 

Figure 4. The distribution of time spent when using automated and manual (static) chamber systems. Below is a comparative timeline showing the time savings and speed to result for an automated system.

Choosing the Right Analyzer


Accuracy is important, however most modern laser-based systems meet or exceed the needs for chamber systems. 


Fast measurements (~1 – 0.5 Hz) provide the most benefit for increasing sample size and reducing chamber closure times.

Power Consumption

Power consumption varies widely amongst systems, but only the most power-light systems can be powered using simple solar or wind-arrays.


Most laser-based systems are meant to be somewhat stationary; however, companies are increasingly offering portable backpack-style systems.

Sample Handling

For closed, dynamic chamber systems, recirculation and leak free operation is critical. Additionally, maximum spatial coverage is determined by the flow rate of the analyzer and pump.

Choosing the Right Chamber

The eosAC series of automated soil flux chambers provide continuous data collection and seamless integration with Picarro, LGR and Gasmet analyzers. Increase spatial coverage by connecting up to 12 eosAC’s to a single analyzer via our eosMX multiplexer. Process your data with ease using the eosAnalyze-AC software, compatible with data from all supported analyzers.