the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 03 Aug 2026
Evaluating N2O Analyzers for Urban Monitoring: Evidence for Underestimated Emissions
Abstract. Nitrous oxide (N2O) is a potent greenhouse gas and ozone-depleting substance. Approximately 42 % of anthropogenic N2O emissions are thought to be emitted from urban areas. We tested three N2O analyzers for their suitability to measure N2O in urban areas from tower and mobile platforms. All three analyzers (Aerodyne SuperDUAL, Aeris MIRA Ultra, and LI-COR LI-7820) have sufficient precision for urban measurements but require different amounts of calibration and attention for best results. Using these analyzers, we observed tower-level enhancements at least 3 times larger than predicted from the best available inventory for the NYC region. Co-measurement of carbon monoxide (available on all but the LI-7820) were used to identify combustion-related enhancements of N2O. Mobile driving confirmed that traffic related emissions for NYC are captured by the inventory and that wastewater treatment is the most likely source for the missing N2O emissions.
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- CC1: 'Comment on egusphere-2026-3919', Andrew Whitehill, 09 Aug 2026 reply
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The manuscript "Evaluating N2O Analyzers for Urban Monitoring: Evidence for Underestimated Emissions" by Andrew Hallward-Driemeier et al. evaluates and compares three commercial nitrous oxide (N2O) monitoring instruments (Aeris Technologies, Aerodyne Research, and LI-COR Environmental) under a variety of conditions and provides recommendations to improve data quality from them. The manuscript also provides an analysis of tower-measurements compared to inventories and mobile measurements of traffic emissions and wastewater treatment emissions. The paper is framed as two loosely-connected narratives (the instrument evaluation and comparison, the measurement campaign and inventory evaluation) – I will comment mostly on the first, as it is within my area of expertise. This is not meant to be a comprehensive review of this manuscript.
This is a very useful manuscript with helpful data for the community (on both methods as well as N2O sources and inventories) and is within the scope of Atmospheric Measurement Techniques. I believe several improvements could be made to make the manuscript stronger prior to publication, which I outline below.
General Comments
Lack of quantitative results and uncertainties
The biggest issue with the methodological sections of this paper is the lack of quantitative results and associated uncertainties. Similar AMT manuscripts (e.g. Commane et al. 2023) generally report more numerical results (in the text or tables), with uncertainties where possible. The main text should have actual numerical results (with uncertainty bounds) rather than general comments or rankings ("All three analyzers outperformed their reported 1-second precision in our tests.", L183; " None of the analyzers showed a significant change…", L207, etc.). The abstract is also written vaguely (e.g. "sufficient precision for urban measurements…"). I recommend the authors be more precise and quantitative when describing their data and results. Almost all measured / modeled values and statistical results are assumed to be exact (reported without uncertainties).
Methodological descriptions could use improvement
Additional description of the methods (flow paths, part numbers, assumptions, etc.) for the experimental sections would make this manuscript much stronger. Additional descriptions of the modeling and associated assumptions would improve the manuscript as well.
Aerodyne water vapor correction
The authors apply a literature-derived water broadening coefficient from Kostinek et al. (2019) to their Aerodyne data. However, they do not provide any details or equations about how they implement this correction. Kostinek et al. (2019) use JFIT software rather than TDLWintel (Aerodyne standard software) and derive specific water vapor corrections (dilution and pressure broadening) applied using specific equations using their specific analysis stack. If the authors of this manuscript drop the Kostinek et al. (2019) broadening coefficients directly into TDLWintel software without proper modification then they are artificially introducing a water vapor bias in their N2O spectral retrieval algorithm. This is very clearly seen in Figure 1B, where there is a significant linear water vapor dependence of the Aerodyne that is indicative of a poorly-chosen water vapor broadening coefficient. Authors need to provide additional justification for the choices they made here. At a minimum, they should describe how they applied the water broadening corrections (assumptions, equations, etc.), why they were necessary (e.g. demonstrate with data that they are superior to the default parameters), and justify their choice of Kostinek et al. (2019) parameters even when using a different fitting code with different assumptions. If authors want to apply appropriate water vapor corrections they should really characterize the broadening for their specific system, software retrieval, and instrument / cell conditions and apply that rather than just applying literature values.
Specific Comments and Suggestions:
L4-L6: Suggest authors include quantitative estimates of precision instead of ambiguous statements like "sufficient precision"
L6-L7: Suggest authors include quantitative values for comparison of tower-level enhancements versus NYC inventories. Also recommend the authors specify which inventory they are using – "best available inventory" is ambiguous.
L19-L25: Authors make lots of claims about budgets and estimates (including numerical values) but do not include uncertainties. One of the core arguments of the papers is that emissions inventories underestimate emissions – it is important to include uncertainties in the emissions estimates and budgets to be able to assess the validity of the argument and the degree of under-estimation.
L32-L34: This implies N2O / CO correlations only occur from fossil fuel combustion. However, biomass burning and other non-fossil combustion sources also co-emit N2O and CO
L51-L52: The Aerodyne SuperDUAL QCL/ICL uses direct or differential infrared absorption spectroscopy in a Herriott multipass absorption cell (see L79). This is not a cavity-enhanced technique – it does not rely on an optical cavity or typical physics employed by cavity-enhanced techniques (CEAS, CRDS, etc.).
L85-L89: See General Comments…
Section 2.1.2.: Authors need to provide additional information about how they set up and configured their Aerodyne sampling. What frequency did they perform spectral backgrounds (e.g. hourly with N2 as in Commane et al., 2023, every 5-10 minutes as in Kostinek et al, 2019)? What frequency did they perform calibrations? The use of regular calibrations or spectral backgrounds is a critical factor affecting the performance of Aerodyne systems and authors do not provide any details about this.
L109-L110: Authors should provide additional details into how they performed their Allan-Werle variance experiments – was it done in a laboratory environment (temperature stability is critical for most of these measurements), what flow rates (cylinder depletion effects relate to flow rate), flow path, etc. Were all instruments measured at the same time? What N2O concentration was the Allan Variance performed at?
L110: It is interesting for authors to apply a specific method (Allan-Werle variance) without citing any of the core papers describing the method (e.g. Werle et al., 1993, etc.).
L117: Minor point, but the degree and magnitude of CO2 leakage across Nafion membranes is an important parameter that has been disagreed upon in the literature (but generally agreed to be relatively small). If the authors note significant CO2 leakage that could be important beyond the scope of the paper, or could indicate an issue with their Nafion membrane. Additional details (e.g. membrane type, leak rate across membrane, or even just CO2 leakage magnitude) would be useful here but not critical.
L134 – L139: The traceability is good (NOAA-2006A) but authors should provide values for the concentrations of their primary calibration cylinders – what range did their calibrations span compared to their measurements?
L183: Authors should include a table here with numerical results (reported 1-s precision, measured 1-s precision, etc.) rather than just a figure. See, e.g. Table 3 of Commane et al. (2023) as an example.
Section 3.1.3:
Authors should include quantitative results here (or in an associated table) instead of general descriptions of the pattern.
Section 3.1.4:
Authors should quantitatively define the "limits sufficient to capture urban N2O variability" as well as report quantitatively the "noise of all three analyzers"
R2 shows strong (but not great) correlation but nothing about the relative accuracy of the analyzers after calibration. I recommend something like a histogram of absolute or relative differences in the measurements between instruments. Given the different response times, this is probably best done on some time-average (5-minute, 1-hour) versus 1 Hz data (or done after applying appropriate time shifts and smoothing to account for instrument response time).
Given differences in flow rates, cell size, and instrument response the 1 Hz data is not expected to agree between instruments, yet it was the chosen interval for inter-instrument comparison. There is also no discussion of time-correction / time-alignment / clock synchronization between instruments, which would be critical for 1-s data comparison.
L224-L225: "The different cell sizes of the analyzers and instrumental noise account for any remaining discrepancies"
This is stated with minimal explanation or justification. Although it is likely correct, authors should show (at least qualitatively) why the discrepancies make sense given their knowledge of cell sizes, flow rates, and noise (which they know for each instrument).
Section 3.2.3: A more thorough wind-direction analysis (or even a few wind rose plots showing the relationship between wind direction and N2O enhancement) or footprint analysis would make the source argument much stronger and more quantitative than the current general observations. Comparing estimated sources with land-use (where is the WWTP compared to the air sources during the peaks? Are there also other potential sources – parkland, industrial sources, transportation hubs, etc. – in similar directions?).
Comparing N2O and CH4 data might also significantly strengthen the WWTP hypothesis (see, e.g., Gålfalk and Bastviken, 2025). According to the manuscript, CH4 was also measured, so why not use it to support your argument?
Figure 1: Why is σ2 in units of ppbv and not ppbv2? Does not σ have units of ppbv?
Datasets:
These should be reviewed and cleaned-up. A brief (non-comprehensive) overview revealed several issues:
allan_variance.csv
The columns seem to be incorrectly labeled here. CO_qcls increases monotonically throughout the test and N2O_aeris is around 15 ppbv and N2O_licor is around 180 ppbv.
humidity_test.csv
Possibly column mislabeling? H2O_aeris seems to be constant around 250 through the entire series, whereas H2O_qcls and H2O_licor vary.
calibration_data.csv
Random N2O jumps to 1540 ppbv (e.g. 03/06/2025) versus 370.26 is not well explained