the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
15 Jan 2021
15 Jan 2021
Measurement report: Characterization of uncertainties of fluxes and fuel sulfur content from ship emissions at the Baltic Sea
- 1Climate Research Program, Finnish Meteorological Institute, Helsinki, Finland
- 2Department of Automotive and Mechanical Engineering, Metropolia University of Applied Sciences, Vantaa, Finland
- 3Aerosol Physics Laboratory, Faculty of Engineering and Natural Sciences, Tampere University, Tampere, Finland
- 4Meteorological and Marine Research Program, Finnish Meteorological Institute, Helsinki, Finland
- 5Air Quality Management Department, Estonian Environmental Research Centre, Tallinn, Estonia
- 6City of Turku, Turku, Finland
- 1Climate Research Program, Finnish Meteorological Institute, Helsinki, Finland
- 2Department of Automotive and Mechanical Engineering, Metropolia University of Applied Sciences, Vantaa, Finland
- 3Aerosol Physics Laboratory, Faculty of Engineering and Natural Sciences, Tampere University, Tampere, Finland
- 4Meteorological and Marine Research Program, Finnish Meteorological Institute, Helsinki, Finland
- 5Air Quality Management Department, Estonian Environmental Research Centre, Tallinn, Estonia
- 6City of Turku, Turku, Finland
Abstract. Deposition of gaseous compounds and nanoparticles from ship emissions was studied by micrometeorological methods at Harmaja in the Baltic Sea. The gradient method was used to measure fluxes of SO2, NO, NO2, O3, CO2, and Ntot (number concentration of nanoparticles). In addition, the fluxes of CO2 were measured by the eddy covariance method. Distortion of the flow field caused by obstacles around the measurement mast was studied by applying a computation fluid dynamic (CFD) model. This was used to establish the corresponding heights in the undisturbed stream, and the wind speed as well as the turbulent parameters at each of the established heights were recalculated for the gradient model. The effect of waves on the boundary layer was taken into consideration, because the Monin–Obukhov theory used to calculate the fluxes is not valid in the presence of swell. Uncertainty budgets for the measurement systems were constructed to judge the reliability of the results. No clear fluxes across the air-sea nor sea-air interface were observed for SO2, NO, NO2, NOx (= NO + NO2) or O3, while a negative flux was observed for Ntot with a median value of −0.23 × 109 m−2 s−1 and an uncertainty range of 31–41 %. For CO2, while both positive and negative fluxes were observed, the median value was −0.0036 mg m−2 s−1 with uncertainty ranges of 25–36 % and 30–60 % for the GR and EC methods, respectively. Ship emissions were responsible for deposition of Ntot while they had a minor effect on CO2 deposition. The fuel sulfur content (FSC) of the marine fuel used in ships passing the site was determined from the observed ratio of SO2 and CO2 concentrations. A typical value of 0.40 ± 0.06 %, was obtained for FSC, which is in compliance with the contemporary FSC limit value of 1 % in the Baltic Sea Area. The method to estimate the uncertainty of FSC was found to be accurate enough for use with the latest regulations, 0.1 % (Baltic Sea Area) and 0.5 % (Global Oceans).
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Jari Walden et al.
Status: final response (author comments only)
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RC1: 'Comment on acp-2020-1086', Anonymous Referee #1, 31 Jan 2021
Walden et al use a gradient method to investigate sea-atmosphere fluxes of various species. The detection limit of the gradient method is not sufficient to observe exchange fluxes for most gases. The authors report particle deposition fluxes, likely originating from ship emissions. Additionally FSC is assessed. The FSC in my opinion is the most interesting part of the manuscript, why weren’t concomitant NOx and particle plumes tracked, as this would seem a natural extension of the experiment. As for the flux analysis a number of major issues arise when reading the paper. While presenting CFD calculations to support the measurement site setup, it is not clear whether stationarity criteria were fulfilled due to passing ships and associated plumes being advected over the site. I disagree that stationarity is primarily characterized by concentration trends, which are part of the longer wave spectrum, often filtered out by turbulence averaging intervals. There are better ways to investigate stationarity (see standard textbooks on micrometeorological data pre-processing). As such the interpretation of fluxes needs to be evaluated carefully, because many fundamental criteria often implied for flux measurements might not be fulfilled. This might also relate to different footprints of individual levels of the gradient tower (e.g. is the lowest level even seeing the water surface or partially also influenced by the island?)
The fact that ship plumes on the order of a few seconds were observed suggests that homogeneity and stationarity was largely not fulfilled for quantifying fluxes from ships.
A comparison between CO2 eddy covariance fluxes and gradient measurements is shown, but it is not indicated what QAQC criteria (e.g. u*, ICT, stationarity,??) were used to filter data, and how much of the original data was used for the analysis after QAQC filtering. Were storage fluxes considered? To that end it would also be good to quantitatively compare the two flux methods for CO2, as it could help validating the gradient method. In this context I would expect to see a scatter plot and regression of both fluxes, - how well did the two methods really compare?
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RC2: 'Comment on acp-2020-1086', Anonymous Referee #2, 29 Mar 2021
General
The paper by Wang et al. focuses on flux measurement uncertainties and sulfur content from ship emissions. The measurements are conducted by gradient method for selected trace gas pollutants and supplemented by eddy covariance measurements of CO2 from a 9 m mast located at a coastal site and on a research vessel 2 km SSW into the sea. Quantifying emission rates from moving ships is certainly not trivial and the number of challenges encountered by the authors is simply impressive. While the paper shows a large effort in conducting the measurements, at least in this version, I would have reservations to the data interpretations and whether they can fully capture ship emissions using the presented approach. The major result from the paper are measured FSCs from the ships all of which did not exceed the EC regulation limit. Overall, I found this paper interesting for the focus on ship emissions, but there are inconsistencies in the data and the paper shows a high potential for further analysis and more coherent presentation of the results.
Major comments
- The major question is how well the assumptions of the gradient and EC methods worked for this heterogenous coastal site. If the large short-term episode (e.g. in SO2 or CO2) occupies only a fraction of the flux integration period, the episodic/spike data would most likely make it nonstationary regardless of whether other micromet variables were stationary or not. The stationarity test should be conducted on each flux tracer including the CO2 data.
- The gradient method also requires accurate measurements at two different heights. If the systematic offset between the instruments (SI Figure S1) was not corrected for, it would lead to large errors in calculated vertical GR fluxes. It is unclear how the data in Fig. S1 were used to correct/cross-calibrate the instruments when the correlation slope differs from 1.
- In the GR method, the authors rely on the assumption that the eddy diffusivity for heat transfer is the same as that for gas mass transfer (e.g. L.81). This could lead to large uncertainties which should be calculated independently for each chemical species. The comparison of GR and EC sensible heat fluxes could have been relatively easy and a good start in comparing the EC and GR methods.
- It would have been great to see a more quantitative comparison for GR and EC methods for CO2 (and for heat). However, from Fig. 9 it is clear that the CO2 fluxes agreed rather poorly, where for example between 08/28 12 PM and 08/29 12 PM, the gradient data show all negative values while EC data are scattered in a broader range mostly positive values but often changing the flux sign. The relative difference between the methods for most of the measured period therefore largely exceeds the uncertainties stated in the abstract (25-36 % and 30-60 % for the GR and EC methods, respectively). For this reason, I am finding highly suspicious the exact same median value for GR and EC CO2 fluxes reported in Table 1. I agree with the comment of the other referee that the scatter plot would have reflected more clearly how both methods worked. If the agreement does not work well for CO2, the question is why and whether the gradient flux method was valid for SO2 and other reported trace gases.
- The flux footprint contribution does not seem to be discussed. The data could give a completely different picture if the ship was outside the footprint (depositing fluxes to the site expected) compared to when the ship would be inside the footprint (emission fluxes expected). It is therefore challenging to attribute any enhancement to the ships without the knowledge of what the footprint was and how it was changing.
- Given the moving point source within likely changing footprint (not uniform at the two heights) I am not convinced that the chosen approach was optimal for quantifying emission rates from ships. There are other methods such as wavelet analysis which could be more appropriate to measure intermittent or short-term emission episodes (e.g. Steiner et al., 2011; Misztal et al., 2014) which are not dependent on stationarity criteria.
- I could not find it in the main text and SI, so I am curious if the data were subjected to coordinate rotation and how close to zero was the average vertical wind speed w? A small tilt of the sonic anemometer could greatly skew the flux data.
- There is no mention about how the lag time was derived for each integration period or if a constant value was used. I am particularly concerned about the potentially incorrect lag time because the CO2 flux was changing sign from one period to the other like, for example, from 28 to 30 Aug (Fig. 9a). It would be great to see how a peak in the covariance function looked like and if the lag time was stable.
- The data quality control is not presented clearly. It would be great to know what criteria were used and which data were actually rejected. For instance, Figures 8 and 9 show the data for when M-O theory was not fulfilled. If it is important to show these low-quality data they could be shown in grey so it is clear that they were rejected and are not distracting from observing potentially good data.
- Conclusions lack the main take-home messages. Practically entire conclusions are spent on emphasizing high uncertainties and challenges and not pointing out the main results or findings. Was the goal to say that the methods did not work at all or that they might potentially work with some improvements? Including the major findings based on the valid data (FSCs?) and further analysis of the remaining data (especially NOx) could significantly improve the manuscript.
Specific comments
- What inlet was used for sampling ultra fine particles?
- SI Figure S2 shows how uncertainty increases closer to the detection limit which is a nice demonstration. However, it is unclear how the data below the detection limit were treated. I suggest to consult Helsel (1990).
- What was the message the multipanel Figure S3 was meant to come across? Is it suggested that the absolute uncertainties exceeded almost all the data values? It could perhaps be clearer to show the relative uncertainties as shaded areas.
- In the uncertainty budget, I would suggest the authors describe the systematic and random errors as well as the treatment of data below the detection limit. It is unclear if the data have been corrected for the systematic error.
- Eq. 10, the value of the 0.232 multiplier seems somewhat off when using the emission factor from Petzold et al. Was a different EF value used instead?
- Figure 1, poor resolution, I could not read the text.
- Figure 3, panel a) low resolution CFD figure, I could not read the legend. It would be useful to add in the text how exactly CFD was used to correct the data and if it was a constant or time-dependent correction.
- Figure 5, these trajectories show long-range transport. Can these be zoomed to the measurement site?
- Figure S1. Make x and y axes consistent. Show the 1:1 line. Were these data used to correct the instruments? How?
- I am surprised that the uncertainty is shown in Table S1 for the nonstationary periods as I do not think it is meaningful. The nonstationary periods should have been rejected. Did the CFD calculation correct only for the horizontal wind speed?
- What was the frequency distribution of CO2 fluxes (FFT spectrum)? As the flux data collection was conducted only at 1 Hz (L. 275), were the data corrected for high frequency losses?
References
Helsel, D.R.: Less than obvious-statistical treatment of data below the detection limit. Environmental science & technology, 24(12), pp.1766-1774, 1990.
Misztal, P. K., Karl, T., Weber, R., Jonsson, H. H., Guenther, A. B., and Goldstein, A. H.: Airborne flux measurements of biogenic isoprene over California, Atmos. Chem. Phys., 14, 10631–10647, https://doi.org/10.5194/acp-14-10631-2014, 2014.
Steiner, A. L., Pressley, S. N., Botros, A., Jones, E., Chung, S. H., and Edburg, S. L.: Analysis of coherent structures and atmosphere-canopy coupling strength during the CABINEX field campaign, Atmos. Chem. Phys., 11, 11921–11936, https://doi.org/10.5194/acp-11-11921-2011, 2011.
Jari Walden et al.
Data sets
Measurement report: Characterization of uncertainties of fluxes and fuel sulfur content from ship emissions at the Baltic Sea Jari Walden, Liisa Pirjola, Tuomas Laurila, Juha Hatakka, Heidi Pettersson, Tuomas Walden, Jukka-Pekka Jalkanen, Harri Nordlund, Toivo Truuts, Miika Meretoja, and Kimmo K. Kahma https://doi.org/10.5281/zenodo.4439605
Jari Walden et al.
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