the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 24 Mar 2025
Sectoral attribution of greenhouse gas and pollutant emissions using multi-species eddy covariance on a tall tower in Zurich, Switzerland
Abstract. Eddy covariance measurement of species that are co-emitted with CO2, such as carbon monoxide (CO) and nitrogen oxides NO and NO2 (NOx), provides an opportunity to attribute a total measured net flux to individual source or sink categories. This work presents eight months of continuous simultaneous measurements of fluxes (F ) of CO2, CO, NOx, methane (CH4), and nitrous oxide (N2O) from an urban tall-tower (112 m agl) in Zurich, Switzerland. Median daily fluxes of FCO2 were 1.47x larger in the winter (Nov–Mar) as opposed to summer (Aug–Oct) months (10.9 vs. 7.4 μmol m−2 s−1); 1.08x greater for FCO (30 vs. 28 nmol m−2 s−1); 1.08x greater for FNOx (14 vs. 13 nmol m−2 s−1); 1.01x greater for FCH4 (13.5 vs. 13.3 nmol m−2 s−1); and not statistically significantly different for FN2O. Flux ratios of FCO/FCO2 and FNOx /FCO2 are well characterised by inventory molar emission ratios of stationary combustion and road transport in cold months. In warm months both FCO/FCO2 and FNOx /FCO2 systematically exceed expected inventory ratios during the day, while no statistically significant seasonal difference is observed in FNOx /FCO, indicating biospheric photosynthetic activity. A linear mixing model is proposed and applied to attribute half-hourly FCO2 , FCO, and FNOx to stationary combustion and road transport emission categories as well as determine the biospheric FCO2 . Flux attribution is reasonable at certain times and from certain wind directions, but over-attributes CO and NOx fluxes to road traffic and CO2 fluxes to stationary combustion, and overestimates photosynthetic CO2 uptake.
- Preprint
(15494 KB) - Metadata XML
- BibTeX
- EndNote
Status: open (until 27 May 2025)
-
RC1: 'Comment on egusphere-2025-1088', Martijn Pallandt, 18 Apr 2025
reply
General
The authors set out to create a unique multispecies dataset of urban fluxes (comprising of CO2, CO, NOx CH4 and N20) to test a new method to partition urban GHG fluxes, specifically into stationary combustion, road transport, and biosphere components. Both this dataset and the methods are novel and of importance since there is a great need for tools to properly partition fluxes in heterogeneous environments. Overall, the writing is clear, the reasoning is sound and the manuscript follows a logical order. Two problems arise during this research. One being unrealistic low values in NOx measurements which are then scaled to local measurements. The other is that the novel partition method struggles in this high flux, high heterogeneity environment, resulting in a mixture of reasonable and unrealistic partition results. The authors are clear on these limitations and suggest linking this method to more components, flux footprints, and temporal explicit emission inventories as potential improvements. Even though the results aren’t entirely as envisioned at the start, they are still valuable and serve as an important stepping stone for further research on this or similar partition methods. As an in-situ case study this research sheds a light on seasonal and daily patterns both in observed and ratios of GHG fluxes in Zurich, and is able to characterize this city as a net source of CO2, CO, NOx, CH4 and N20. It was an interesting read and I'm curious how this research further develops.
Specific
20: Cities leading these efforts if quite a statement, the references here need some work or the claim needs to be modified: the European commission report doi 404ed on my side, the stad Zurich website probably has no place here ->“ Informal or so-called "grey" literature may only be referred to if there is no alternative from the formal literature.”(and this is just a website not an archived report). A quick scan of the Lwasa executive summary seems to point out that GHG emissions will very likely increase through cities modernization construction, and further urbanization, though they note there is a need for decarbonization for cities.
108: Is there a particular reason why these months were chosen? A clearer distinction between winter and summer would be observable if June, July and August were chosen instead. Related to this at line 156 /table 2: the winter period is considerably longer at 5 months vs 3 and probably with worse weather, it can be informative to indicate the distribution of (successful) samples between summer and winter.
165: It is unfortunate when equipment malfunctions, time dependent in-situ measurements as these cant be redone and you have to work with what you have. However, this section goes fairly quickly from noting that observations didn’t match expectations to scaling them up. Some essential steps appear to be missing.
Was the source of the error investigated, (e.g. was it a mechanical or a calibration issue), why did it only affect NOx fluxes, and have similar problems been reported with this instrument? Without identifying the source of the issue, it is hard to justify a correction.
The NOx flux was corrected based on local measurements, but why not perform an actual recalibration/correction with calibration gasses? That would give more certainty in its accuracy than a local measurement which adds several layers of uncertainty such as transport and their measurement errors. It would also allow for additional tests on any drift, to verify that the adjusted slope is stable over time.
I assume that the throughout the paper the adjusted NOx values are used as if they were the actual measurements, and the increased uncertainty was not propagated throughout the rest of the analysis. In either case, please state clearly how this was handled.
179: Why this 4x4 square and not the entire city or a larger part of the tower footprint?
Figure 3: Not essential but a 6th panel in this style with temperatures would be interesting since these would be the main drivers in differences between winter and summer.
301: If you can conclude based on figure 5 that the ratio can be a mixture of road transport and stationary combustion, why not a combination of respiration and road transport?
350 / figure 6: This explanation mainly fits the winter period. Summer 2-8 is largely indistinguishable from 6-7 when I look at the figure, and the afternoon rush seems to start at 12 already. Summer seems different from winter here.
375 /figure 7A: The nocturnal fluxes would be the yellow area around y0.7 x1.3 just outside the dashed lines? Without a temporal component to this figure, it is not directly clear which the nocturnal NOx fluxes are. Can you clarify.
400 and 418: What can be done about this unrealistic allocation that goes well beyond measured quantities? While the section starting around 427 discusses improvements in general, can you give specific advice on preventing this problem in future cases? Maybe adding more components or limiting allocation to know maxima?
453-455: If I followed the equations in section 2.5 correctly al equations for the final partition components include the NOx term directly or indirectly. Therefore, wouldn’t this affect all ratios?
Did you at some point test the sensitivity of this model to FNOx? Even if you are not certain of your measurements it would give an indication of the impact of such errors.
456 - 465: I am somewhat surprised to find new methods and results after the discussion of the model’s limitations. And it is not clear to me what you have done here. These two paragraphs and table 6 can benefit greatly from a rewrite to clarify the method and intent. I would advise to add a subsection to the methods section describing this sensitivity analysis in a bit more detail, and discuss these results probably before ~427
Specifically:
- Table 4 nor its description make mention of a and b. what is exactly being combined here?
- In the section on the linear mixing model no mention is made of the mmol mol resolution, in which way is it different here?
- You are testing the sensitivity of the linear mixing model to changes in what exactly? The discussion in the second paragraph also doesn’t make clear what sensitivity has been tested here. A typical sensitivity analysis tests a range of values of certain parameters.
- Continuing with table 6: The first ‘% of total’ refers to table 3 where you have 5 categories and the second ‘relative’ to only the two listed in this table 6 (sc and rt)? Please clarify.
- And the model outputs should be compared to the relative column since the model only uses the sc and rt categories? If the model outputs are per wind direction, it doesn’t make much sense to compare them to the general reference inventories, wouldn’t it be more interesting then to have 5 relative inventory references: all and the 4 directions?
- In line 496 it is mentioned the sensitivity to reference inputs was tested here?
468 That would be a valuable continuation of this research, though if you continue with this dataset really aim to get a proper recalibration of the NOx data.
477-489: Since not everyone might look at the methods (in detail) a disclaimer on NOx uncertainty in the second or third paragraph is appropriate.
466: “complex and heterogeneous urban environment may ultimately pose too great a challenge for the application of such a model with fixed emission factors over long periods of time and large flux footprints.” And 501: “ however in the complex and heterogeneous urban environment this information is difficult to exploit on its own.”
This is unfortunate, since these are the environments where flux partitioning is especially important. In a homogeneous environment partitioning of fluxes is of lesser importance. Hopefully next steps will improve on this method further.Technical
~427: not essential but you could put a section break here with everything after ~427 a discussion of the assumptions and where they are met/failed.
483: tower..
Citation: https://doi.org/10.5194/egusphere-2025-1088-RC1
Viewed
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 246 | 47 | 6 | 299 | 3 | 3 |
- HTML: 246
- PDF: 47
- XML: 6
- Total: 299
- BibTeX: 3
- EndNote: 3
Viewed (geographical distribution)
| Country | # | Views | % |
|---|---|---|---|
| United States of America | 1 | 80 | 27 |
| Germany | 2 | 47 | 16 |
| China | 3 | 25 | 8 |
| Switzerland | 4 | 15 | 5 |
| Sweden | 5 | 12 | 4 |
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
- 80