the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 01 Nov 2022
Pandemic Restrictions in 2020 highlight the significance of non-road NOx sources in central London
Will Drysdale
James D. Lee
Carole Helfter
Eiko Nemitz
Stefan Metzger
Janet F. Barlow
Abstract. Fluxes of nitrogen oxides (NOx = NO + NO2) and carbon dioxide (CO2) were measured using eddy covariance at the BT Tower in central London during the coronavirus pandemic. Comparing fluxes to those measured in 2017 prior to the pandemic restrictions and the introduction of the Ultra-Low Emissions Zone (ULEZ) highlighted a 75 % reduction in NOx emissions between the two periods but only a 20 % reduction in CO2 emissions and a 32 % reduction in traffic load. Use of a footprint model and the London Atmospheric Emissions Inventory (LAEI) identified transport and heat and power generation to be the two dominant sources of NOx and CO2 but with significantly different relative contributions for each species. Application of external constraints on NOx and CO2 emissions allowed the reductions in the different sources to be untangled identifying that transport NOx emissions had reduced by > 75 % since 2017. This was attributed in part to the success of air quality policy in central London, but crucially due to the substantial reduction in congestion that resulted from pandemic reduced mobility. Spatial mapping of the fluxes suggests that central London was dominated by point source heat and power generation emissions during the period of reduced mobility. This will have important implications on future air quality policy for NO2 which until now, has been primarily focused on the emissions from diesel exhausts.
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Notice on discussion status
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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Preprint
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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.
- Preprint
(2082 KB) - BibTeX
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- Final revised paper
Journal article(s) based on this preprint
Samuel J. Cliff et al.
Interactive discussion
Status: closed
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RC1: 'Comment on egusphere-2022-956', Anonymous Referee #2, 13 Nov 2022
General comments:
This manuscript presents eddy covariance NOx and CO2 fluxes measured in central London during the COVID pandemic and shows by comparing to pre-pandemic measurements that NOx emissions significantly decreased due to reductions in traffic load. While a number of studies have revealed NOx reductions during COVID from regional to global scales using satellite observations or surface monitors coupled with models, this study offers insight from another angle with the eddy covariance technique and delves into source attribution of NOx and CO2 reductions. The study draws attention to urban power and heat generation, which was identified as the major source of NOx in the area during the lockdowns. My overall comment is that the authors should discuss the extent to which their findings relate to and differentiate from existing studies in the field, and highlight the fact that this is the first evidence using eddy covariance measurements in a megacity. The only other eddy covariance measurements I am aware of that looked at this topic were made in Innsbruck, Austria (Lamprecht et al., 2021. doi: 10.5194/acp-21-3091-2021). Aside from this, I also have concerns regarding the method used for comparing the two periods and the conclusions drawn, mainly the lack of discussion on other factors that may vary between the two periods. Please see below the details.
Specific comments:
L65: Can you provide an estimate of lag time of your measurements for each species?
L71: I understand that many technical details were described in Drysdale et al. (2022) for the 2017 measurements so only a summary is provided, but please at least cite the relevant work(s) here for those who would like to read further on the instruments and methodologies. For example, how exactly do you achieve “NOx free air”? What are the accuracies and precisions of your instruments?
L87: The NOx, CO2, and meteorological measurements all have different sampling rates. How did you synchronize the measurements to calculate the hourly fluxes?
L93: Please specify the sampling period of your 2017 measurements because this is an important detail. Based on my understanding the 2017 fluxes were only available from March to August, whereas your 2020/21 data covered a full year. Some of your comparisons between the two periods included only those in the same months (Figure 5) but the others compared the full year of 2020/21 to the six months of 2017 (Figure 2 and 5). How much bias would these comparisons of unequal lengths cause?
Besides, the potential influence of meteorology between different times of the year/between different years was never discussed. If your argument is that the emissions decreased due to anthropogenic reasons you need to prove that the meteorological effects were negligible or at least provide an uncertainty estimate. Can you show some meteorological data from your anemometer such as the average temperature diurnal profile for each of the two periods, or the average boundary layer height from ERA5?
In addition, did you compare the instrument performance in 2020/21 to 2017 to make sure the measurements were not affected by any system degradation such as long-term drifts?
L133: Can you also mark the date of full removal of lockdown restrictions on Figure A3?
L137: Can you describe in more detail how you calculated the reduction percentages from the diurnal profiles?
L176: What is the rationale behind the assumption that CO2 emissions reduction scales linearly with traffic load reduction?
Figure 2: Interesting that the CO2 and NOx flux diurnal profiles both show a bimodal pattern peaking around noon and again around 3-4 pm. I thought the peaks would appear closer to the morning and evening rush hours, especially in the case of NOx given that Figure 3 suggests transport was the main source of NOx emissions. Can you explain why they display this pattern?
Also, are the error bars on the diurnal profiles the 1σ standard deviation of fluxes? Is the greater variability of fluxes in 2020/21 mainly due to the difference in temporal coverage?
Figure 5: While the differences between the 2020/21 and 2017 fluxes are noticeable, it is difficult to get a sense of how the data actually correlate with traffic flow from the figure. Can you calculate the correlation coefficients statistically? This will also aid your argument “The greatly reduced correlation with traffic load for the easterly 2020/21 data in Figure 5 is further evidence that the dominant source in this direction is heat and power generation.” (L235-236)
Technical corrections:
L20: has additional
L44: other external stimuli
L46: surface-atmosphere exchange
L69: was converted into
L113: artic lorries?
Figure 2: Consider moving this figure to a different place. It is currently placed awkwardly between the “Results and Discussion” section title and the first paragraph.
Figure 4: Check equation labels on the figure: CO2 Eq. (2) and NOx Eq. (3)?
Citation: https://doi.org/10.5194/egusphere-2022-956-RC1 -
RC2: 'Comment on egusphere-2022-956', Anonymous Referee #1, 17 Dec 2022
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2022/egusphere-2022-956/egusphere-2022-956-RC2-supplement.pdf
- AC1: 'Comment on egusphere-2022-956', Samuel Cliff, 26 Jan 2023
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2022-956', Anonymous Referee #2, 13 Nov 2022
General comments:
This manuscript presents eddy covariance NOx and CO2 fluxes measured in central London during the COVID pandemic and shows by comparing to pre-pandemic measurements that NOx emissions significantly decreased due to reductions in traffic load. While a number of studies have revealed NOx reductions during COVID from regional to global scales using satellite observations or surface monitors coupled with models, this study offers insight from another angle with the eddy covariance technique and delves into source attribution of NOx and CO2 reductions. The study draws attention to urban power and heat generation, which was identified as the major source of NOx in the area during the lockdowns. My overall comment is that the authors should discuss the extent to which their findings relate to and differentiate from existing studies in the field, and highlight the fact that this is the first evidence using eddy covariance measurements in a megacity. The only other eddy covariance measurements I am aware of that looked at this topic were made in Innsbruck, Austria (Lamprecht et al., 2021. doi: 10.5194/acp-21-3091-2021). Aside from this, I also have concerns regarding the method used for comparing the two periods and the conclusions drawn, mainly the lack of discussion on other factors that may vary between the two periods. Please see below the details.
Specific comments:
L65: Can you provide an estimate of lag time of your measurements for each species?
L71: I understand that many technical details were described in Drysdale et al. (2022) for the 2017 measurements so only a summary is provided, but please at least cite the relevant work(s) here for those who would like to read further on the instruments and methodologies. For example, how exactly do you achieve “NOx free air”? What are the accuracies and precisions of your instruments?
L87: The NOx, CO2, and meteorological measurements all have different sampling rates. How did you synchronize the measurements to calculate the hourly fluxes?
L93: Please specify the sampling period of your 2017 measurements because this is an important detail. Based on my understanding the 2017 fluxes were only available from March to August, whereas your 2020/21 data covered a full year. Some of your comparisons between the two periods included only those in the same months (Figure 5) but the others compared the full year of 2020/21 to the six months of 2017 (Figure 2 and 5). How much bias would these comparisons of unequal lengths cause?
Besides, the potential influence of meteorology between different times of the year/between different years was never discussed. If your argument is that the emissions decreased due to anthropogenic reasons you need to prove that the meteorological effects were negligible or at least provide an uncertainty estimate. Can you show some meteorological data from your anemometer such as the average temperature diurnal profile for each of the two periods, or the average boundary layer height from ERA5?
In addition, did you compare the instrument performance in 2020/21 to 2017 to make sure the measurements were not affected by any system degradation such as long-term drifts?
L133: Can you also mark the date of full removal of lockdown restrictions on Figure A3?
L137: Can you describe in more detail how you calculated the reduction percentages from the diurnal profiles?
L176: What is the rationale behind the assumption that CO2 emissions reduction scales linearly with traffic load reduction?
Figure 2: Interesting that the CO2 and NOx flux diurnal profiles both show a bimodal pattern peaking around noon and again around 3-4 pm. I thought the peaks would appear closer to the morning and evening rush hours, especially in the case of NOx given that Figure 3 suggests transport was the main source of NOx emissions. Can you explain why they display this pattern?
Also, are the error bars on the diurnal profiles the 1σ standard deviation of fluxes? Is the greater variability of fluxes in 2020/21 mainly due to the difference in temporal coverage?
Figure 5: While the differences between the 2020/21 and 2017 fluxes are noticeable, it is difficult to get a sense of how the data actually correlate with traffic flow from the figure. Can you calculate the correlation coefficients statistically? This will also aid your argument “The greatly reduced correlation with traffic load for the easterly 2020/21 data in Figure 5 is further evidence that the dominant source in this direction is heat and power generation.” (L235-236)
Technical corrections:
L20: has additional
L44: other external stimuli
L46: surface-atmosphere exchange
L69: was converted into
L113: artic lorries?
Figure 2: Consider moving this figure to a different place. It is currently placed awkwardly between the “Results and Discussion” section title and the first paragraph.
Figure 4: Check equation labels on the figure: CO2 Eq. (2) and NOx Eq. (3)?
Citation: https://doi.org/10.5194/egusphere-2022-956-RC1 -
RC2: 'Comment on egusphere-2022-956', Anonymous Referee #1, 17 Dec 2022
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2022/egusphere-2022-956/egusphere-2022-956-RC2-supplement.pdf
- AC1: 'Comment on egusphere-2022-956', Samuel Cliff, 26 Jan 2023
Peer review completion
Journal article(s) based on this preprint
Samuel J. Cliff et al.
Samuel J. Cliff et al.
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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.
- Preprint
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