the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 19 Jun 2025
Validation of the Fugitive Emission Distributed Sampling (FEDS) system: A mobile, multi-inlet system for continuous emissions monitoring
Abstract. The National Physical Laboratory (NPL) has developed and trialled a mobile and remotely-operated Fugitive Emission Distributed Sampling (FEDS) system for continuous measurements of emissions at the facility spatial scale. FEDS is capable of both locating and quantifying emission sources over long-term periods and has been deployed at sites around the UK to monitor methane emissions from the natural gas network, landfill, and waste treatment. This work presents validation activities using a controlled release facility (CRF) to test the performance of the measurement system and two reverse dispersion models (Airviro and WindTrax) for emission quantification. Emissions were quantified using prior knowledge of release timings as well as in the absence of this knowledge. High variability in wind direction was shown to negatively impact emission quantification accuracy (especially for Airviro). Emission results were improved by removing periods of high wind variability (low wind persistence) from analysis. Both models performed better when using daily-averaging periods for emissions (Airviro RMSE = 0.37 kg h-1; WindTrax = 0.29 kg h-1) over shorter averaging periods, such as hourly data (Airviro RMSE = 0.77 kg h-1; WindTrax = 2.19 kg h-1). Emission rates were shown to be sensitive to the specified source release height for both models, with discrepancies in model release height relative to the true release height of more than 0.5 m yielding less accurate results. Furthermore, it was shown that emission results were less accurate when using concentration data input from fewer sampling locations, although it would be remiss to recommend a minimum density of sampling locations for a given area based on a single validation study.
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RC1: 'Comment on egusphere-2025-1451', Anonymous Referee #1, 06 Aug 2025
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Review of: Validation of the Fugitive Emission Distributed Sampling (FEDS) system: A mobile, multi-inlet system for continuous emissions monitoring.
General: The work presented here feels a little “work in progress” rather than a true validation of the system. Whilst it is a useful step in the validation process of FEDS, I am a little concerned that it doesn’t demonstrate the capabilities in a truly rigorous way in comparison to the intended use scenarios and that a follow-up piece of work is needed. I would request that the term “validation” is removed from the title as I think more is needed – maybe “first assessment” is more appropriate?
I believe this work is worth publishing after revisions, as it lays the groundwork for the FEDS system. A part II controlled release study paper would be welcomed – possibly alongside the first site level measurements?
There are a number of significant improvements and increase in scope that I would like the authors to consider. Some should be feasible within the boundaries of the current dataset, but others I would like them to be mindful of as they design their next controlled release experiments to assess and improve the system.
- The work should be as rigorously compared to other systems attempting to achieve the same / similar goals. The work of the Stanford and Colorado State Universities that have done significant work assessing continuous monitoring system performance should be heavily cited and compared as directly as possible through the creation of comparable performance metrics. As it currently stands, I have no direct reference in the paper as to whether this system performs well, averagely or poorly compared to other systems.
- The use of the models chosen (and why not others) is not terribly well justified. I would have liked to have seen a comprehensive look at the possible choices and then reasoning for using these and not others. There are solution possibilities using tools such as GRAL (https://gral.tugraz.at/) or even an algorithmic approach to quantification such as that being proposed by LBL for abandoned oil and gas infrastructure assessment (https://egusphere.copernicus.org/preprints/2025/egusphere-2025-344/egusphere-2025-344.pdf)
- There is no “non-model” baseline to compare to. Given the simplicity of the set up I would have liked to have seen a simple analysis of the results identifying when a concentration is elevated above background by a certain threshold and then plotting a back trajectory line from the node using the measured wind fields, and using that to determine source location and provide a comparative success index without the need for a model.
- I wonder if there is room for improvement on the inlet design? There a plenty of design set ups that could have borrowed heavily from the eddy covariance community that have utilized fast 10 Hz instruments with rapid throughput of gas which would massively reduce the dead analysis period that needs to be removed and allow much faster switching between nodes. I would encourage this to be investigated and some spreadsheet calculations to work out if this is worth redesigning for in the future.
- Somewhat adding to the previous point, the low time resolution of the system (one measurement period per node per hour) is very limiting and will cause severe problems when trying to scale up to sites with intermittent sources or variable emission rates. It may be okay for purely providing an overarching emission estimate from long duration deployment, but without the ability to also act as a mitigation tool the role of it as a method may be very limited in the context of continuous monitoring options.
- As you have identified, understanding the wind is key. For future work, I would recommend siting a small 2D sonic at each node so that exact wind information can be obtained for each location. For any site with topography or structures, quite dramatic changes in wind can be seen across a site due to shielding and slope effects which would be important information for the modelling.
- The potential improvement of fully utilizing the atmospheric stability is mentioned in the paper, but not followed up. It seems that adding this analysis in is within the remit of the work as it should demonstrate whether this is something that should be included in future analysis?
- Why does the performance of the two models vary so significantly in terms of false positive detections? From an operator perspective this is a key metric as no one wants to send staff out to fix things that don’t exist. This should have more detail around it and understand why this is occurring. This should be considered a significant problem for the windtrax solution.
- There is a hint in the conclusions that low cost sensors were also trialed – I would love to see the data on this and some more details as to how it was set up. A null result is still very useful and may help others to not continue down a similar path of method.
Detailed line by line:
Abstract:
L15: Add some details of the CR - number of sources, release rate ranges, what is being simulated.
L19: Without knowledge of the true emission rate these values are a little meaningless. If the emission rate is 100kg hr, then these are wonderful. If it is 2kg hr, less so... Maybe replace throughout with % of true emission.
L21:23: Feels more like discussion that abstract.
Main Text:
L31: efficient, accurate and transparent monitoring
L41: Discuss snapshot vs continuous
L46: Put in context of reporting requirements such as the new EU regualtions or voluntary programmes such as OGMP2.0? These are already in place and have specific requirements around needs.
L74: The CR needs to be better described here. Is it fully blind, single point or multiple point etc...?
L79: I wouldn’t consider custom Gaussian plume out of reach of commercial teams.
L83: Specify what is considered high-performance
Fig 1: Instrument labelled as uMEA
L114: The FMEA may sample at 1Hz, but what is the turnover time of the cell and therefore what is the true sampling resolution of the system?
L125: Expand this section so that the reader can understand the lags and data invalidation periods. 60s seems like a very long time to clear a portion of line if the lines to each node are being continually pumped. I’d like to see some of this analysis in the main paper.
L129: Is this really sufficient for traceability? I think this quality of calibration is quite poor and I would have expected better QC on this. Can this be replicated in the lab now with more cal gases to demonstrate that instrument performance is as expected over the measurement range seen.
L169: I do have questions around wind persistence and whether it would become less of a controlling problem if you weren’t having to average everything to hourly timesteps. I suspect that there isn’t sufficient data to dig into this further, but I would consider looking at minute by minute enhanced methane readings and correlating with wind direction to see if that gives better data for locating an emission source.
L200: As this is stated to be a validation study, I would like to see more information and justification around the design of the experiment – especially given that it is stated that this system will be used for landfill quantification, among other things.
Fig 3. Can the wind direction be plotted as dots so that there is no 360 jumps in the data.
L260 section: I’d like to see a probability of detection metric determined for the set up if at all possible.
Fig 4. This is not at all intuitive and I would give serious consideration to redefining how this is presented and instead look at methane excess over background as the primary metric rather than just atmospheric mole fraction as I think it would provide much more immediate understanding
L434: Reference issue
L458-462: Whilst these models may perform to the stated stats, I don’t get a sense of understanding as to why they are performing well or poorly (with the exception of the discussion of the wind persistence). Is there more to the modelling than this / more nuance?
L497: Surely measuring more methane downwind from the source can’t be considered a conclusion?
L513: Give % errors so that we can see how performance was without having to know what the release rates were.
Conclusions: General – I’d suggest shortening and tightening up once the major corrections are sorted. It is a little long and unconcise as it stands.
Citation: https://doi.org/10.5194/egusphere-2025-1451-RC1
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