the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 08 Apr 2025
Aerial Estimates of Methane and Carbon Dioxide Emission Rates Using a Mass Balance Approach in New York State
Abstract. Accurate greenhouse gas (GHG) emissions inventories are vital for climate mitigation as they can identify areas of need and ensure effective policy and regulation in reducing GHG emissions. Several studies have shown that self-reporting GHG inventories are undercounting methane emissions across all anthropogenic sectors showcasing an increasing need to validate the inventory with direct measurements. This study carried out aerial observations and emission rates of methane and carbon dioxide across multiple sectors in New York State (NYS). Emission rates were calculated for each of the sources using a mass balance method and were subsequently compared to the 2021 Environmental Protection Agency GHG Reporting Program (EPA GHGRP) Inventory. Landfills were the source of the highest observed methane emission estimates, ranging from 161–3440 kg/hr. There was also significant variation in observed emissions within facilities between seasons indicating a significant influence from meteorology. Observed carbon dioxide emission estimates were dominated by combustion facilities followed by landfills. Comparisons with the inventory show that methane emissions averaged over ten observed landfills are underestimated by a factor of 2. However, out of the ten landfills, five landfills had observed methane emission estimates significantly higher than the inventory value, four landfills had an inventory value within the uncertainty range of the observations, and one landfill observed emission estimate was markedly lower than the reported inventory estimate. Seneca Meadows Landfill was the highest emitter from the measurements and was ~4.3x higher than what was reported to the 2021 EPA GHGRP Inventory. The difference in emissions between landfills could be due to operational differences or waste quantities. NYS can use this information to inform the NYS GHG Inventory and improve emission estimation methodologies to better depict actual emissions.
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Status: open (until 15 May 2025)
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RC1: 'Comment on essd-2025-135', Anonymous Referee #1, 23 Apr 2025
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The paper by Catena et al. presents aerial measurements of methane (CH4) and carbon dioxide (CO2) emission rates from various sectors in New York State (NYS), including landfills, combustion facilities, wastewater treatment plants (WWTPs), and agricultural sites. Using a mass balance approach based on Gauss’s theorem, the study estimates emission rates and compares them with the 2021 EPA Greenhouse Gas Reporting Program (GHGRP) inventory, revealing that methane emissions from landfills are, on average, underestimated by a factor of 2. The findings provide valuable data to refine the NYS GHG Inventory and support climate mitigation efforts. However, the study suffers from several limitations: the methodology lacks clarity, the uncertainty analysis is inadequate, the scientific merit is limited, and the results may be biased due to insufficient data. Although the paper is well-structured with a logical flow, it lacks in-depth discussion of the findings and their implications. I recommend a major revision to address these shortcomings.
Major concerns:
- Methods:
- The description of the mass balance method using Gauss's theorem is overly brief and lacks critical details. I only understand the method by reading Conley et al. (2017). I recommend the authors expand the methods section to include a detailed explanation of the mass balance approach, explicitly addressing the storage term, data interpolation, and altitude binning, and explain the similarities and differences compared to the study of Conley et al. (2017), clarifying any deviations from their method. Conley assumed that the vertical mixing at the upper boundary is zero, and their schematic in Fig. 1 illustrates that the tracer mixing ratio vertical gradient at the upper boundary is close-to zero, which defends their assumption. However, this paper does not explicitly mention this assumption.
- The authors do not present their flight data (i.e., spatial structure of the data) at all. It is difficult to visualize data and confirm if these data accurately capture the plume. I suggest that the authors present their data in SI. In addition, a schematic in the main text would help to understand the method.
- The city-level emission in Table 5 might be problematic. It is unclear if the measurements adequately sample the downwind region of the entire city. It would be better to show the flight track and wind fields.
- Methods:
- The uncertainty analysis is limited to turbulent flux variability between flight loops, ignoring other sources such as plume extrapolation below the lowest flight altitude, instrument precision, wind measurement errors, or seasonal representativeness. The assumption of a constant methane mixing ratio, from the surface to the lowest flight level, is poorly justified, especially given the potential for strong near-surface gradients in urban or landfill settings. It is unclear how the authors quantify the PBL height and whether lower measurements are all within the PBL. It is unclear how PBL diurnal cycle alters the results. Overall, it is not clear what the sources of uncertainty are. It would be better to provide a high-level summary here rather than only pointing out to other papers.
- The reliance on single-day measurements (or two days for some facilities) introduces potential bias, as emissions may vary significantly due to operational or meteorological changes. The paper acknowledges seasonal differences but does not assess whether one single-day measurement is representative of annual emissions. I can see that the authors mention this potential caveat at the end of the paper, but this should be highlighted very early in the paper. In addition, it is not clear whether the reported 2021 EPA GHGRP emission should be interpreted as annual average or at the time that can be directly compared with airborne measurements. It seems like all the measurements are collected during the daytime. Would the missing of nighttime measurements bias the flux quantification, or in other words, is there a diurnal cycle in the emissions?
- Facilities with non-detectable emissions (e.g., some WWTPs) are inadequately explained, possibly due to sampling limitations in urban areas or insufficient plume capture.
Minor comments:
- Table 3 rows are not lined up.
- L143: For each loop, are you flying at the same height?
- L190-192: This statement might be significantly weakened as you only have one day of data per season. Unless you can somehow show that the emission is consistent over the course of entire season, and your measurement is not sensitive to any sorts of synoptic scale disturbances.
- L193: How does ambient pressure affect CH4 emission?
- L194: Wind speed and direction would change the redistribution of GHG emissions. However, your method accounts for the wind to inversely derive flux from concentration.
- L194: It is unclear what you mean by soil moisture. Soil moisture of the landfill?
- Figure 2: It is unclear what each point represents, and what time of the year is shown. Please print the name of each site on x-axes. Are error bars assumed to be 1-sigma uncertainty?
- Figure 5: This figure does not provide much useful information. Your table is good enough to raise the point.
- L304: Is there a way you can showcase that the extra CO2 emissions you observed comes from biogenic CO2 emissions?
- Although it may be beyond the scope of this study, have you considered using a Lagrangian transport model (i.e., STILT, etc.) to translate concentration to flux, which I believe is much more robust?
- Maybe it is beyond the scope of ESSD, which focuses on presenting the data, but I am curious about the implications of the results, such as why some landfills show larger discrepancies than others. The paper mentions operational differences and waste quantities but lacks specific data or analysis to support these claims.
- The tables (2–5) are comprehensive but you could put in them SI to enhance clarity.
- A lot of the references are not correctly formatted.
Citation: https://doi.org/10.5194/essd-2025-135-RC1
Data sets
Raw Aerial Observations of Methane and Carbon Dioxide at Several Sectors in New York State in 2021 Alexandra Catena and Mackenzie Smith https://doi.pangaea.de/10.1594/PANGAEA.979845
Emission Rate Estimates of Methane and Carbon Dioxide at Several Sectors in New York State in 2021 Alexandra Catena and Mackenzie Smith https://doi.pangaea.de/10.1594/PANGAEA.979843
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