the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 03 Mar 2026
Measured methane emissions from a metropolitan wastewater treatment lagoon in Victoria Australia are substantially higher than report emissions based on emission factors
Abstract. Wastewater treatment facilities contribute ~8 % of global anthropogenic methane (CH4) emissions. Accurate measurements of CH4 emissions not only improve greenhouse gas (GHG) emission estimates from the facilities but also expand our understanding of operational impact on emissions, thus enabling the development of effective mitigation strategies. In this study, CH4 emissions were measured during summer and winter seasons at an aerobic lagoon at a large sewage treatment plant in Australia. Line-averaged CH4 concentrations were measured by open-path lasers and CH4 fluxes were calculated using inverse-dispersion modelling. Methane fluxes showed temporal and spatial variations over the measurement periods, and correlated with wastewater dissolved methane, flow rate, and aerator operation. The annual GHG emission of 79,593 tCO2-e yr-1, represents ~25 % of CH4 production captured by the anaerobic digestion pot, and is approximately 2‒3 times higher than the National Greenhouse and Energy Reporting Scheme (NGERS) reported emissions of the aerobic lagoon.
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Status: final response (author comments only)
- RC1: 'Comment on egusphere-2026-6', Anonymous Referee #2, 24 Mar 2026
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RC2: 'Comment on egusphere-2026-6', Anonymous Referee #1, 28 Apr 2026
This manuscript presents direct methane flux measurements from an aerobic wastewater treatment lagoon and compares the observed fluxes with inventory-based estimates. The topic is important because wastewater lagoons remain poorly constrained sources of methane, and field measurements are still limited in the literature. The dataset has potential value, and the paper addresses a relevant gap for greenhouse gas accounting. However, in its current form, I think the manuscript requires major revision before publication. The main conclusions are potentially important, but several aspects of the analysis and interpretation need stronger justification. In particular, the representativeness of the measurements, uncertainty treatment, site characterization, and extrapolation to broader inventory implications require more detailed discussion.
My Specific comments are as follows:
- What motivated the authors to study an aerobic pond instead of an anaerobic pond, given that the latter could be a larger concern for methane production? Is it possible to provide any comparison between these two types of ponds?
- The atmospheric transport model is 3-dimensional. However, the authors’ measurements only happened at ground level. How could this fact be addressed in uncertainty analysis? Could drone-based vertical measurement of methane concentrations improve the credibility of the emission rate estimation?
- The measurement only happened in Summer and Winter, which was not representative enough for the annual case. Also, the manuscript would benefit from a clearer justification of whether the monitoring period is representative of longer-term methane emissions.
- In L151-L154, could you elaborate more on how the WindTrax model works, instead of treating it like a black box? What assumptions does the model use? What uncertainties does it bring? And does it work equally well on point source and areal source?
- In L151-L154, did the authors treat the entire pond as a homogeneous areal source? If so, what uncertainty can that bring?
- In L182, what does “stratified sampling” mean?
Citation: https://doi.org/10.5194/egusphere-2026-6-RC2
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General Comments:
The manuscript presents an interesting comparison between measured methane concentrations and reported emission factors at a large metropolitan wastewater treatment lagoon. The use of Inverse-Dispersion Modelling (IDM) with open-path lasers (OPL) is a robust approach for non-intrusive monitoring; however, the current manuscript has significant gaps regarding spatial weighting, micrometeorological artifacts, and the representativeness of the upscaled annual data. Without addressing these potential biases, the conclusion that emissions are 2–3 times higher than NGERS reports may be premature.
Specific comments:
1. How does the "touchdown" coverage (mentioned as >20% in Section 2.5) vary across the pond surface? Does the weighting reflect the actual spatial concentration gradient?
2. Is the 8:00 AM peak a result of a biological process, or is it a micrometeorological artifact caused by the breakup of the nocturnal boundary layer? At 8:00 AM, the atmosphere often transitions from stable to unstable, which can "dump" accumulated methane toward the sensors. In the early morning, the "surface layer" might not be fully developed. Are you seeing a real biological emission peak, or is it just the "fumigation" of methane trapped near the water surface overnight being released as the sun hits the pond?
3. I am wondering why a simple linear weighting (0.67/0.33) is superior to a more robust spatial interpolation. If the wind direction shifts even slightly, the "footprint" of what those lasers "see" changes drastically. Did you perform a sensitivity analysis on those weights?
4. Was the background laser (OPLC34) moved to account for different northerly wind angles (e.g., NNE vs. NNW)? If not, the (C/Q)sim could be biased by "dirty" upwind air that wasn't properly subtracted.
5. Did the cross-calibration (Section 2.2) account for the difference in lower-detection limits between the two brands? If the "East" laser is less precise, the uncertainty in the "high emission" zone is actually higher than in the "low emission" West zone.
6. You calculated an annual emission based on a 5-week summer campaign and a 7-week winter campaign. But wastewater chemistry (BOD/COD) and microbial activity aren't just seasonal; they are operational. Was the "flow rate" or "aerator schedule" during these 12 weeks truly representative of the other 40 weeks of the year? If the facility had a "high load" period during their measurement window, the "2–3 times higher than NGERS" claim might be an overestimation of the annual total.