the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 10 Jul 2024
Measurement Report: The effects of SECA regulations on the atmospheric SO2 concentrations in the Baltic Sea, based on long-term observations at the Finnish Utö Island
Abstract. The designation of the Baltic Sea as a Sulphur Emission Control Area (SECA) in May 2006, with subsequent tightening of regulations in 2010 and 2015 has reduced the sulphuric emission from shipping traffic. This study, focusing on impacts of SECA on observed SO2 concentrations, provides a long-term analysis of 1–minute time resolution air quality data from 2006 to 2020 at Utö island (Baltic Sea), supported by the predictions from the Ship Traffic Emission Assessment Model (STEAM). Additionally, hourly data from 2003 to 2005 is utilized to investigate changes due to the SECA limits set in 2006. The observed SO2 concentrations at Utö have continuously decreased since 2003 due to an overall decrease in SO2 emissions in Northern Europe, combined with reduced emissions from shipping traffic due to SECA regulations. Three–year average SO2 concentrations dropped from pre–SECA (2003–2005) to post–SECA periods (2007–2009, 2011–2013, 2016–2018) by 38 %, 39 %, and 67 %, respectively. No clear trends were observed in the concentrations of other pollutants measured. In addition to time series analysis, we investigated wind direction resolved SO2 concentrations for two selected years and studied the changes in ship plumes of one vessel regularly passing by Utö. This study brings out the importance of long–term, high time-resolution air quality observations at remote marine research stations, in the vicinity of a heavily trafficked ship lane, providing possibility for both quantitative and qualitative analyses of the impacts of regulatory environmental legislation.
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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Journal article(s) based on this preprint
Interactive discussion
Status: closed
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RC1: 'Comment on egusphere-2024-1703', Mingxi Yang, 12 Jul 2024
This paper reports on the long term (2003-2020) measurements of atmospheric trace gases and aerosols from an island in Finland, with a particular focus on the impact of shipping regulation on SO2. The authors show that the SO2 concentrations have decreased significantly, especially since 2015, while NOx, PM2.5 haven’t really changed. This is clearly a very valuable timeseries dataset, thanks to its length. However at present I don’t feel like I’ve learned much new from reading this paper. There have been earlier studies that report on the reductions in atmospheric SO2 in coastal areas following the IMO regulations, which this paper fails to acknowledge. Other questions that could be (but not currently) addressed this paper include:
- Fuel sulfur content and the rate of compliance by ships
- Does the observed trend in SO2 reflect what one expects (e.g. based on atmospheric transport modelling with STEAM emission and terrestrial S sources)?
- Atmospheric processing of trace gases
Specific comments
Line 66. This paragraph is ok in isolation. But it’s not something this work will address. It’s probably better to remove or significantly shorten it.
Instead, before line 79 it would be useful to review previous work on ship SO2 emissions (including time series measurements similar to this study, e.g. doi:10.5194/acp-15-5229-2015 and http://www.atmos-chem-phys.net/16/4771/2016/). What are the knowledge gaps that this paper can fill? Is it e.g.
- impact of regulation on atmospheric pollutant level?
- scrubber vs. low sulfur fuel?
- rate of compliance from ships?
- attribution of emission to different ship types?
- atmospheric processing and transformation of trace gases
4.1 there is nothing wrong with this section on its own. However I don’t feel like it contributes very much to the paper at the moment. I guess the key message is that S emission from non-shipping sectors has been declining gradually, while S emission from shipping has decreased in step wise fashion following the IMO regulations (which we would’ve expected even without STEAM model)?
I think this section can be made more powerful if the authors implement these emissions in an atmospheric transport model, see what the predicted change in SO2 is, and then compare the model with the observations.
Line 158. SO2 lifetime was estimated to be only 0.5 day to the west of the UK. http://www.atmos-chem-phys.net/16/4771/2016/ in cloud oxidation is probably the largest sink.
Fig.2 how come the SOx emission from shipping sector hasn’t decreased in step-wise fashion, corresponding to the regulations? Is it because the emission also includes outside of SECA?
Table 1. a bit more detail on the SO2 measurements would be useful (even if this info had been reported previously elsewhere). E.g. how was it blanked and calibrated?
Line 169-170 this is a repeat
Line 176. ‘until’
Table 2. what’s N(%)?
Figure 5. how come percentiles and median are not shown for data over the first few years? Is it because the data were hourly, not minutely? In general, I don’t really see the value of presenting/analyzing minutely data for this section. Hourly data would be perfectly fine for looking at long term trends. Minutely data contain much more measurement noise, especially for SO2.
Line 238. How were ‘peaks’ identified/defined? What’s the minimum NO concentration in this calculation? Have you accounted for any possible lag between the SO2 and NO data due to imperfect time synchronization or different instrument response times?
Figure 6. NO reacts rapidly with O3 and has a strong diurnal cycle and SO2:NO will too. Was CO2 not measured at the site, which would’ve enabled the estimation of the fuel sulfur content? If not, SO2:NOx still seems better than SO2:NO, though NOx emissions can vary significantly depending on the ship/weather conditions.
Section 4.1 it’s a bit odd to have this section here, when you just chosen the western sector (180-360) for the SO2:NO analysis above. Wouldn’t be better to do the wind sector analysis first, and then apply the according wind sector to SO2:NO?
Figure 7. similar to figure 5, the SO2 axis is cut off at zero. Are you discarding all negative SO2 data? I don’t think that is the best approach. The negative numbers (due to measurement noise) need to be kept in in order for the stats to be representative.
Figure 9. I don’t doubt that the SECA regulation has been effective. However here the SO2 and NOx concentrations were not evaluated with consideration of plume dilution. Would’ve been best to normalize both gases to CO2 plume. If that’s not possible, at least look at SO2:NOx ratio (which does seem to be lower after 2015).
In general, I don’t find that the case study has added much to the paper. Are there more information that can be teased out? E.g. fuel sulfur content before vs after 2015? Did the ship install a scrubber?
Citation: https://doi.org/10.5194/egusphere-2024-1703-RC1 - AC1: 'Reply on RC1', Androniki Maragkidou, 01 Nov 2024
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RC2: 'Comment on egusphere-2024-1703', Anonymous Referee #2, 26 Jul 2024
Review of the preprint “Measurement Report: The effects of SECA regulations on the atmospheric SO2 concentrations in the Baltic Sea, based on long-term observations at the Finnish Utö Island” by A. Maragkidou et al., submitted to Atmospheric Chemistry and Physics
The manuscript describes a long term observation of air pollutants on the Finnish Utö island. The analysis is focused on SO2 concentrations in order to demonstrate the effects of SECA regulations to reduce sulphur emissions from shipping. It is an interesting paper that deserves publication, but improvements and clarifications are needed.
Major general comments:
It isn’t exactly clear how you use the STEAM data in your analysis. Please include it in the data interpretation or leave it out.
I am not convinced that we can learn something from the RoRo ship case study (section 4.5). Please explain this better in the paper or remove this part.
Please include a paragraph/section about the limitations of your study and about the uncertainties. This should include a discussion about the representativity of the observations for a larger region. Can we really say something about the compliance to the SECA rules in the Baltic Sea when we have approx. 8 ships passing by per day?
Major specific comments:
Line 135-137: Which data was used for the STEAM model (2006 – 2020)? Why was there a change in AIS data source for the RoRo ship in 2016?
Line 151/152 and section 4.5: I do not see the purpose of the case study looking at one RoRo ship. What do you want to demonstrate? What can we learn from this?
Line 164/165: What is the data source for this graph?
Line 179/180 and Fig. 3: Why are PM2.5 emissions only available with one decimal place?
Line 180 and Fig. 3: What happens with the NOx emissions in 2017? After a steady decrease until end of 2017 something looks different in the data with an increase in 2018. It think you cannot say that the emissions remained stable. It would also be interesting to see the CO2 emissions from STEAM in order to see the effect of increased ship traffic vs. more efficient fuel use. Besides the sulphur content in ship fuels, the total amount of fuel burned has an influence on SO2 concentrations. This is not much discussed in the paper.
Fig. 5, b): Why does the median for PM2.5 reach 0 between 2016 and 2018? It looks like the median and the mean are lower in 2015-2017 and then go up again in 2018. Is this connected to a change in instruments?
Fig. 5, c): It looks like NO is lower since 2014, this holds in particular for the median. This is also visible in the data in the appendix and it is in contrast to what you say in the text. Can you comment on it? Is it because of the change in instruments? Could you please elaborate on the effects of instrument changes in general?
Fig. 6: Why is there no data in 2011? And could you give some statistical information? How large is the reduction in 2015-2020 compared to the earlier years? And how does this compare to the expected reduction because of more stringent SECA rules.
Line 155 and Fig. 7: Please enhance the figures with some statistical information or use a separate table for this. How does the data compare to that from all wind sectors?
Line 267: It is not clear why you select 2019 as the year with lower sulphur emissions from shipping. Why not 2016/17/18? Having possible changes in emissions from other sources in mind, 2019 seems to be not the preferred choice.
Line 273/274 and Fig. 8 a) and b): It seems that SO2 mean and/or median does not depend very much on wind direction. Can you comment on this?
Fig.8 a) and b): The reader has the impression that violet values are 0 – 0.5 µg/m³, yellow values are 0.5 – 1.5 µg/m³, and red values are >1.5 µg/m³. This is in contrast to the legend and the caption.
Lines 286-329, Section 4.5: As said before, this section does not provide new insights. You may completely skip it unless you describe better, what is new and what can be learned from it.
Line 335: The STEAM results are not used very much. You should improve this.
Line 339/340: You would underpin this statement with a trend analysis including statistical significance of the trend. It is also in contrast to steady emission reductions in many emission sectors in Europe (Fig. 2). Therefore, you may a few words on this.
Line 340/341: This could also be caused by changes or variations in emissions
Line 350/351: I would like to read some words about the possibilities to check compliance to the NECA since 2021. What are the prospects for the future for these observations related to air pollution from shipping?
Minor comments:
Line 98: Is there really no local wood burning that may have an effect on SO2 and PM2.5 concentrations? Or were these events removed from the data?
Line 131: These vessel categories are not well defined. What is “small” and what is “large”. Please give more details. And why is there no further distinction of cargo ships into e.g. container ships, tankers, bulk cargo, …? I would assume that STEAM considers more categories than six.
Line 158: Correct: “is therefore originating”
Line 173: Effects of the pandemic on emissions were not visible before 2020.
Line 190/191: “The amount of ship traffic has been fairly constant during this period.” repeats what was said before.
Line 205, Table 2: Please explain STD, N, TBA. Are there no units for STD?
Line 232/233: please make clear that these changes always refer to the 2003-2005 values (and not to the previous period).
Line 240/241: Perhaps you want to introduce abbreviations like SWECA2006, SECA2011, SECA2015 to make clear which phase of the regulation you talk about. This might also help at other places (e.g. line 256, but there may be more).
Line 242: replace “during” with “within”
Line 250-258: These paragraphs need improvements of the English language (e.g. articles).
Citation: https://doi.org/10.5194/egusphere-2024-1703-RC2 - AC2: 'Reply on RC2', Androniki Maragkidou, 01 Nov 2024
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2024-1703', Mingxi Yang, 12 Jul 2024
This paper reports on the long term (2003-2020) measurements of atmospheric trace gases and aerosols from an island in Finland, with a particular focus on the impact of shipping regulation on SO2. The authors show that the SO2 concentrations have decreased significantly, especially since 2015, while NOx, PM2.5 haven’t really changed. This is clearly a very valuable timeseries dataset, thanks to its length. However at present I don’t feel like I’ve learned much new from reading this paper. There have been earlier studies that report on the reductions in atmospheric SO2 in coastal areas following the IMO regulations, which this paper fails to acknowledge. Other questions that could be (but not currently) addressed this paper include:
- Fuel sulfur content and the rate of compliance by ships
- Does the observed trend in SO2 reflect what one expects (e.g. based on atmospheric transport modelling with STEAM emission and terrestrial S sources)?
- Atmospheric processing of trace gases
Specific comments
Line 66. This paragraph is ok in isolation. But it’s not something this work will address. It’s probably better to remove or significantly shorten it.
Instead, before line 79 it would be useful to review previous work on ship SO2 emissions (including time series measurements similar to this study, e.g. doi:10.5194/acp-15-5229-2015 and http://www.atmos-chem-phys.net/16/4771/2016/). What are the knowledge gaps that this paper can fill? Is it e.g.
- impact of regulation on atmospheric pollutant level?
- scrubber vs. low sulfur fuel?
- rate of compliance from ships?
- attribution of emission to different ship types?
- atmospheric processing and transformation of trace gases
4.1 there is nothing wrong with this section on its own. However I don’t feel like it contributes very much to the paper at the moment. I guess the key message is that S emission from non-shipping sectors has been declining gradually, while S emission from shipping has decreased in step wise fashion following the IMO regulations (which we would’ve expected even without STEAM model)?
I think this section can be made more powerful if the authors implement these emissions in an atmospheric transport model, see what the predicted change in SO2 is, and then compare the model with the observations.
Line 158. SO2 lifetime was estimated to be only 0.5 day to the west of the UK. http://www.atmos-chem-phys.net/16/4771/2016/ in cloud oxidation is probably the largest sink.
Fig.2 how come the SOx emission from shipping sector hasn’t decreased in step-wise fashion, corresponding to the regulations? Is it because the emission also includes outside of SECA?
Table 1. a bit more detail on the SO2 measurements would be useful (even if this info had been reported previously elsewhere). E.g. how was it blanked and calibrated?
Line 169-170 this is a repeat
Line 176. ‘until’
Table 2. what’s N(%)?
Figure 5. how come percentiles and median are not shown for data over the first few years? Is it because the data were hourly, not minutely? In general, I don’t really see the value of presenting/analyzing minutely data for this section. Hourly data would be perfectly fine for looking at long term trends. Minutely data contain much more measurement noise, especially for SO2.
Line 238. How were ‘peaks’ identified/defined? What’s the minimum NO concentration in this calculation? Have you accounted for any possible lag between the SO2 and NO data due to imperfect time synchronization or different instrument response times?
Figure 6. NO reacts rapidly with O3 and has a strong diurnal cycle and SO2:NO will too. Was CO2 not measured at the site, which would’ve enabled the estimation of the fuel sulfur content? If not, SO2:NOx still seems better than SO2:NO, though NOx emissions can vary significantly depending on the ship/weather conditions.
Section 4.1 it’s a bit odd to have this section here, when you just chosen the western sector (180-360) for the SO2:NO analysis above. Wouldn’t be better to do the wind sector analysis first, and then apply the according wind sector to SO2:NO?
Figure 7. similar to figure 5, the SO2 axis is cut off at zero. Are you discarding all negative SO2 data? I don’t think that is the best approach. The negative numbers (due to measurement noise) need to be kept in in order for the stats to be representative.
Figure 9. I don’t doubt that the SECA regulation has been effective. However here the SO2 and NOx concentrations were not evaluated with consideration of plume dilution. Would’ve been best to normalize both gases to CO2 plume. If that’s not possible, at least look at SO2:NOx ratio (which does seem to be lower after 2015).
In general, I don’t find that the case study has added much to the paper. Are there more information that can be teased out? E.g. fuel sulfur content before vs after 2015? Did the ship install a scrubber?
Citation: https://doi.org/10.5194/egusphere-2024-1703-RC1 - AC1: 'Reply on RC1', Androniki Maragkidou, 01 Nov 2024
-
RC2: 'Comment on egusphere-2024-1703', Anonymous Referee #2, 26 Jul 2024
Review of the preprint “Measurement Report: The effects of SECA regulations on the atmospheric SO2 concentrations in the Baltic Sea, based on long-term observations at the Finnish Utö Island” by A. Maragkidou et al., submitted to Atmospheric Chemistry and Physics
The manuscript describes a long term observation of air pollutants on the Finnish Utö island. The analysis is focused on SO2 concentrations in order to demonstrate the effects of SECA regulations to reduce sulphur emissions from shipping. It is an interesting paper that deserves publication, but improvements and clarifications are needed.
Major general comments:
It isn’t exactly clear how you use the STEAM data in your analysis. Please include it in the data interpretation or leave it out.
I am not convinced that we can learn something from the RoRo ship case study (section 4.5). Please explain this better in the paper or remove this part.
Please include a paragraph/section about the limitations of your study and about the uncertainties. This should include a discussion about the representativity of the observations for a larger region. Can we really say something about the compliance to the SECA rules in the Baltic Sea when we have approx. 8 ships passing by per day?
Major specific comments:
Line 135-137: Which data was used for the STEAM model (2006 – 2020)? Why was there a change in AIS data source for the RoRo ship in 2016?
Line 151/152 and section 4.5: I do not see the purpose of the case study looking at one RoRo ship. What do you want to demonstrate? What can we learn from this?
Line 164/165: What is the data source for this graph?
Line 179/180 and Fig. 3: Why are PM2.5 emissions only available with one decimal place?
Line 180 and Fig. 3: What happens with the NOx emissions in 2017? After a steady decrease until end of 2017 something looks different in the data with an increase in 2018. It think you cannot say that the emissions remained stable. It would also be interesting to see the CO2 emissions from STEAM in order to see the effect of increased ship traffic vs. more efficient fuel use. Besides the sulphur content in ship fuels, the total amount of fuel burned has an influence on SO2 concentrations. This is not much discussed in the paper.
Fig. 5, b): Why does the median for PM2.5 reach 0 between 2016 and 2018? It looks like the median and the mean are lower in 2015-2017 and then go up again in 2018. Is this connected to a change in instruments?
Fig. 5, c): It looks like NO is lower since 2014, this holds in particular for the median. This is also visible in the data in the appendix and it is in contrast to what you say in the text. Can you comment on it? Is it because of the change in instruments? Could you please elaborate on the effects of instrument changes in general?
Fig. 6: Why is there no data in 2011? And could you give some statistical information? How large is the reduction in 2015-2020 compared to the earlier years? And how does this compare to the expected reduction because of more stringent SECA rules.
Line 155 and Fig. 7: Please enhance the figures with some statistical information or use a separate table for this. How does the data compare to that from all wind sectors?
Line 267: It is not clear why you select 2019 as the year with lower sulphur emissions from shipping. Why not 2016/17/18? Having possible changes in emissions from other sources in mind, 2019 seems to be not the preferred choice.
Line 273/274 and Fig. 8 a) and b): It seems that SO2 mean and/or median does not depend very much on wind direction. Can you comment on this?
Fig.8 a) and b): The reader has the impression that violet values are 0 – 0.5 µg/m³, yellow values are 0.5 – 1.5 µg/m³, and red values are >1.5 µg/m³. This is in contrast to the legend and the caption.
Lines 286-329, Section 4.5: As said before, this section does not provide new insights. You may completely skip it unless you describe better, what is new and what can be learned from it.
Line 335: The STEAM results are not used very much. You should improve this.
Line 339/340: You would underpin this statement with a trend analysis including statistical significance of the trend. It is also in contrast to steady emission reductions in many emission sectors in Europe (Fig. 2). Therefore, you may a few words on this.
Line 340/341: This could also be caused by changes or variations in emissions
Line 350/351: I would like to read some words about the possibilities to check compliance to the NECA since 2021. What are the prospects for the future for these observations related to air pollution from shipping?
Minor comments:
Line 98: Is there really no local wood burning that may have an effect on SO2 and PM2.5 concentrations? Or were these events removed from the data?
Line 131: These vessel categories are not well defined. What is “small” and what is “large”. Please give more details. And why is there no further distinction of cargo ships into e.g. container ships, tankers, bulk cargo, …? I would assume that STEAM considers more categories than six.
Line 158: Correct: “is therefore originating”
Line 173: Effects of the pandemic on emissions were not visible before 2020.
Line 190/191: “The amount of ship traffic has been fairly constant during this period.” repeats what was said before.
Line 205, Table 2: Please explain STD, N, TBA. Are there no units for STD?
Line 232/233: please make clear that these changes always refer to the 2003-2005 values (and not to the previous period).
Line 240/241: Perhaps you want to introduce abbreviations like SWECA2006, SECA2011, SECA2015 to make clear which phase of the regulation you talk about. This might also help at other places (e.g. line 256, but there may be more).
Line 242: replace “during” with “within”
Line 250-258: These paragraphs need improvements of the English language (e.g. articles).
Citation: https://doi.org/10.5194/egusphere-2024-1703-RC2 - AC2: 'Reply on RC2', Androniki Maragkidou, 01 Nov 2024
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Meteorological and air quality data Finnish Meteorological Institute https://en.ilmatieteenlaitos.fi/open-data
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Androniki Maragkidou
Tiia Grönholm
Laura Rautiainen
Juha Nikmo
Jukka-Pekka Jalkanen
Timo Mäkelä
Timo Anttila
Lauri Laakso
Jaakko Kukkonen
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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