the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 21 Mar 2022
Insights on estimating urban CO2 emissions using eddy-covariance flux measurements
Abstract. Living in an era of government allocated carbon dioxide (CO2) emissions, knowing the accurate amount of human-induced CO2 becomes very critical. To this end, an in-depth understanding of CO2 emissions in urban areas where human activities are concentrated will be of practical help. With this motivation, we quantify CO2 emission strengths of individual urban activities (i.e. vehicle, industry, heat generation, etc.) based on direct observations of vertical CO2 exchanges at urban-atmosphere interface using Eddy-Covariance (EC) method at Gwangju, Korea (2017.11–2018.08). Day of week difference analysis, together with varying wind sector, grounded from carefully designed measurement set-up, enables us to assess CO2 emission factors (EFs) free from seasonal bias (i.e. heating and urban vegetation); evaluated EFs of traffic from day of week difference was 0.017(±0.011) μmol m-2 s-1 car-1 which is more than 10 times larger than that from simple relation (0.0012 ± 0.0011 μmol m-2 s-1 car-1) between CO2 flux and traffic counts. The CO2 emissions due to the car manufacturing industry within the fetch and heating when air temperatures were lower than 18 °C were quantified as 103.25(±42.18) μmol m-2 s-1 and 2.41(±1.71) μmol m-2 s-1 °C-1, respectively. Urban vegetation uptake was estimated as -1.72 kg C m-2 yr-1 only with EFs traffic inferred from day of week difference indicating possible erroneous estimation in simple relation unless it properly reflects representative seasonal changes in a year. Even though our estimations are conservative EFs due to limitations in corrections of horizontal seepage and vertical storage, we found that both EFs for traffic and heat in latest emission inventory were more than 2.5 times lower than our estimations which indicate the urgency in bottom-up inventory validations.
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RC1: 'Comment on acp-2022-205', Anonymous Referee #1, 26 Apr 2022
Dear Authors,
This study conducted the eddy covariance measurement at an urban area in Korea during a year for evaluating contributions from different emission sources, such as traffic, heating, industrial, and vegetation and validating an emission inventory. Since direct measurements of CO2 emissions are scarce in urban areas, the topic is interesting to potential readers and thus is suitable to the journal. However, due to several limitations in the presented work, I recommend substantial revisions.
Main comments:
- First, the authors did not state the objective of the study in the introduction. Consequently, I hardly justified the main conclusion, new finding, or hypothesis with the data. Furthermore, structure of the manuscript is not well organized. I strongly recommend that the results and discussion sections must be separated for deeper discussions.
- As we know that urban flux measurements were not ideal especially for the instrumentation, I have concerns for the data quality of the eddy covariance measurements. The authors conducted the general quality controls according to Mauder and Foken (2004), but did not provide detailed information. In addition, authors used measured fluxes for all wind directions (Figs. 2, 4) although the flow distortion was expected (Fig. S1). Please show the flow statistics, such as sigma u, v, w per friction velocity for assuring the quality control.
- Traffic CO2 emissions based on the two methods contained substantial uncertainties. First, the traffic counts were measured at a few points within (or outside?) the flux footprint; thus, the estimated emission factor (flux per car) should contain biases. The term emission factor should be inappropriate in this study. Second, more seriously, human activity (e.g., commercial and business activities) could be closely related to traffic count at the diurnal and weekday/weekend scales. The simple regression or weekday/weekend statistics included not only traffic activity but also other human activity. This could overestimate the traffic CO2 emissions.
- For comparison to the inventory, many differences were shown in Fig. 8, but not were well discussed about the concrete reasons. Measured CO2 emissions were several times higher than those by the inventory. Such large discrepancy should be caused by fundamental problems with measurements and/or inventory. The authors must carefully discuss potential problems in the inventory with their calculation methods (e.g., how the inventory estimated emissions in detail). Furthermore, measured CO2 flux also contained missing values, but there was no description how gap-filling was conducted.
Specific comments:
Line 77: “thus easily covers a city scale”. The statement is incorrect. Eddy covariance measurements even using a tall tower typically could not cover the entire city. Furthermore, given the heterogeneous nature of the city, spatial representativeness often hampered interpreting measured CO2 emissions.
Table 1: Range of CO2 flux was vague in terms of their temporal coverage. Such information should be described with annual CO2 emissions or mean flux with specified period (e.g., daytime mean at the annual peak month). The unit of car seems to be strange, because the unit of traffic count should be car per period (e.g., car per sec, car per hour, or car per day).
Line 144: The equation of the covariance is too general and should be removed.
Lines 175-189: As mentioned in the above major comment, DOW method contained uncertainties, because weekday/weekend differences in flux could be associated with differences in traffic as well as whether commercial sectors were open or not. Thus, DOW is influenced by CO2 emissions by commercial sectors.
Fig S3. Please change the color scale for easily distinguishing vegetation and non-vegetation areas. Currently, almost all sectors are colored green or yellow.
Lines 217-218: Please explain more detailed information how the authors correct the traffic density.
Lines 226-233: I could not understand how HDD was used for the analysis, because temperature sensitivity was estimated with air temperature (Fig. 7). Add more information in details.
Lines 240-244: For upscaling CO2 emission to city scale, floor number of buildings must be considered for commercial and residential sectors in addition to aerial coverage.
Fig. 1b: Please show actual photos rather than deformed schematics because readers more easily understand the instrumentation based on the photo rather than the image.
Lines 279-280. I am not sure how the authors wanted to explain using this statement. If the authors wanted to mention air storage or underestimates in turbulent fluxes, discuss more details in a quantitative manner.
Line 292-293. Polar plots in Fig. 4 are interesting, but were not described how is was conducted in the method section. Add detailed methods with relevant citations.
Fig. 5 and lines 309-310. Weekday/weekend difference in cars seems to be marginal. Please explain how differences were statistically significant. The unit of Fig. 5a should be [number per hour]. Furthermore, how are weekday/weekend differences in traffic count consistent among the traffic count sites? How did the range of inconsistency among the sites affect the estimates in the traffic CO2 emission?
Line 308: Be quantitative manners.
Line 364: I cannot understand how HDD was used.
Line 371: As mentioned above, further quantitative discussion is required.
Line 376: I cannot understand the rationale of this statement “were able to estimate from … strategies”. Is this supported by the current data analysis?
Line 392: The CO2 uptake by vegetation is too high. Please see Fig. 2 in Baldocchi (2014) which showed that range of annual CO2 uptake by natural or disturbed ecosystems.
Line 420: Based on the current environment for open data science, “author upon request” seems to be insufficient. Please use public databases, such as KoFlux, FLUXNET, or other open databases.
Reference
Baldocchi, D. 2014. Glob. Change Biol., 20, 3600-3609.
Citation: https://doi.org/10.5194/acp-2022-205-RC1 - AC1: 'Reply on RC1', Kyung-Eun Min, 10 Jun 2022
-
RC2: 'Comment on acp-2022-205', Anonymous Referee #2, 02 May 2022
“Insights on estimating urban CO2 emissions using eddy-covariance flux measurements”Kyung-Eun Min et.al,2022
General comments
This manuscript try to quantify CO2 emission strengths of individual urban activities (vehicle, industry, heat generation et.al.) based on less than one year measurements with Eddy-Covariance (EC) method at Gwangju, Korea. The author estimated CO2 emission factors (EFs) of Traffic/Industry/Heat from the EC measurement, while the plants influence on CO2 exchange including photosynthesis and respiration can be estimated as the net balance of total emissions among all activities with observation (for the estimation of EF of vegetation). Based on their EFs estimations, they found that the annual CO2 emissions of traffic and space heating were more than 2.5 times higher than those of the emission inventory for Gwangju in 2017-2018. However, this experiment setup and data are not reliable. The CO2 flux measuring system was installed on the helideck of the Gwangju city hall, so the building’s effect on the EC measurement could not be ignoring. The results are not robust. On the other side, there are lot of EC towers to measurement the Co2 flux in city since the beginning of 21st century, and some sites have collected more than 10 years dataset. CO2 emission factors (EFs) could not be used to other city as a universal parameter for the estimation of the annual co2 flux in the city. By the way, the model to simulate the co2 flux over city has been published (Jarvi, L., et al.(2019),JGR: Atmos.).This manuscript is not suitable to be accepted by ACP.
Specific comments1) L123-134 “The CO2 flux measuring system was installed on the helideck of the Gwangju city hall,……Our EC system was installed outside of inertial sublayer …and sufficiently lower than the planetary boundary layer.”, is it correct? The measurement is set in Gwangju city hall (90 m above the ground – building height: 85 m, helideck: 3 m and measuring system structure: 2 m), so it is not satisfied the guideline on the flux measurement in the city. The building’s effect on the flow has large influence on the EC measurement.
2) L166-167 ”footprint boundaries were defined to confine 70% of average total flux during the measurement period.”, usually we use the footprint boundaries to cover 90% of average total flux “.
3) L172-174 ” To assess the quantitative contributions of the individual sources, the wind directions were split into two sectors; (1) the Eastern Industrial Area (EIA, 45º-100º) and (2) the Southern Green Area (SGA, 100º-225º), based on whether or not the fetch includes the automobile production plant and urban vegetation .“, it is too simple to assess the quantitative contributions with two sectors division, due to the complex flow field in this city. So the EFs estimation has too large uncertainty.
4) Gwangju city is located in a basin area, while there is a more than 1000m High Mountain in the east of the city. The local circulation due to the terrain should be occurred sometime during the season, and may be interaction with the urban heat island (UHI) in Gwangju city. So the co2 flux measurement may be also influence by the two circulation. This manuscript didn’t consider any information on the topic. By the way, the wind rose (daytime or nighttime) during 2017-2018 could not be found in the text.
Jarvi, L., Havu, M., Ward, H. C., Bellucco, V., McFadden, J. P., Toivonen, T., Heikinheimo, V., Kolari, P., Riikonen, A., & Grimmond, C. S. B. (2019). Spatial Modeling of Local-Scale Biogenic and Anthropogenic Carbon Dioxide Emissions in Helsinki. Journal of Geophysical Research : Atmospheres, 1124(15), 8363-8384.Citation: https://doi.org/10.5194/acp-2022-205-RC2 - AC2: 'Reply on RC2', Kyung-Eun Min, 10 Jun 2022
Status: closed
-
RC1: 'Comment on acp-2022-205', Anonymous Referee #1, 26 Apr 2022
Dear Authors,
This study conducted the eddy covariance measurement at an urban area in Korea during a year for evaluating contributions from different emission sources, such as traffic, heating, industrial, and vegetation and validating an emission inventory. Since direct measurements of CO2 emissions are scarce in urban areas, the topic is interesting to potential readers and thus is suitable to the journal. However, due to several limitations in the presented work, I recommend substantial revisions.
Main comments:
- First, the authors did not state the objective of the study in the introduction. Consequently, I hardly justified the main conclusion, new finding, or hypothesis with the data. Furthermore, structure of the manuscript is not well organized. I strongly recommend that the results and discussion sections must be separated for deeper discussions.
- As we know that urban flux measurements were not ideal especially for the instrumentation, I have concerns for the data quality of the eddy covariance measurements. The authors conducted the general quality controls according to Mauder and Foken (2004), but did not provide detailed information. In addition, authors used measured fluxes for all wind directions (Figs. 2, 4) although the flow distortion was expected (Fig. S1). Please show the flow statistics, such as sigma u, v, w per friction velocity for assuring the quality control.
- Traffic CO2 emissions based on the two methods contained substantial uncertainties. First, the traffic counts were measured at a few points within (or outside?) the flux footprint; thus, the estimated emission factor (flux per car) should contain biases. The term emission factor should be inappropriate in this study. Second, more seriously, human activity (e.g., commercial and business activities) could be closely related to traffic count at the diurnal and weekday/weekend scales. The simple regression or weekday/weekend statistics included not only traffic activity but also other human activity. This could overestimate the traffic CO2 emissions.
- For comparison to the inventory, many differences were shown in Fig. 8, but not were well discussed about the concrete reasons. Measured CO2 emissions were several times higher than those by the inventory. Such large discrepancy should be caused by fundamental problems with measurements and/or inventory. The authors must carefully discuss potential problems in the inventory with their calculation methods (e.g., how the inventory estimated emissions in detail). Furthermore, measured CO2 flux also contained missing values, but there was no description how gap-filling was conducted.
Specific comments:
Line 77: “thus easily covers a city scale”. The statement is incorrect. Eddy covariance measurements even using a tall tower typically could not cover the entire city. Furthermore, given the heterogeneous nature of the city, spatial representativeness often hampered interpreting measured CO2 emissions.
Table 1: Range of CO2 flux was vague in terms of their temporal coverage. Such information should be described with annual CO2 emissions or mean flux with specified period (e.g., daytime mean at the annual peak month). The unit of car seems to be strange, because the unit of traffic count should be car per period (e.g., car per sec, car per hour, or car per day).
Line 144: The equation of the covariance is too general and should be removed.
Lines 175-189: As mentioned in the above major comment, DOW method contained uncertainties, because weekday/weekend differences in flux could be associated with differences in traffic as well as whether commercial sectors were open or not. Thus, DOW is influenced by CO2 emissions by commercial sectors.
Fig S3. Please change the color scale for easily distinguishing vegetation and non-vegetation areas. Currently, almost all sectors are colored green or yellow.
Lines 217-218: Please explain more detailed information how the authors correct the traffic density.
Lines 226-233: I could not understand how HDD was used for the analysis, because temperature sensitivity was estimated with air temperature (Fig. 7). Add more information in details.
Lines 240-244: For upscaling CO2 emission to city scale, floor number of buildings must be considered for commercial and residential sectors in addition to aerial coverage.
Fig. 1b: Please show actual photos rather than deformed schematics because readers more easily understand the instrumentation based on the photo rather than the image.
Lines 279-280. I am not sure how the authors wanted to explain using this statement. If the authors wanted to mention air storage or underestimates in turbulent fluxes, discuss more details in a quantitative manner.
Line 292-293. Polar plots in Fig. 4 are interesting, but were not described how is was conducted in the method section. Add detailed methods with relevant citations.
Fig. 5 and lines 309-310. Weekday/weekend difference in cars seems to be marginal. Please explain how differences were statistically significant. The unit of Fig. 5a should be [number per hour]. Furthermore, how are weekday/weekend differences in traffic count consistent among the traffic count sites? How did the range of inconsistency among the sites affect the estimates in the traffic CO2 emission?
Line 308: Be quantitative manners.
Line 364: I cannot understand how HDD was used.
Line 371: As mentioned above, further quantitative discussion is required.
Line 376: I cannot understand the rationale of this statement “were able to estimate from … strategies”. Is this supported by the current data analysis?
Line 392: The CO2 uptake by vegetation is too high. Please see Fig. 2 in Baldocchi (2014) which showed that range of annual CO2 uptake by natural or disturbed ecosystems.
Line 420: Based on the current environment for open data science, “author upon request” seems to be insufficient. Please use public databases, such as KoFlux, FLUXNET, or other open databases.
Reference
Baldocchi, D. 2014. Glob. Change Biol., 20, 3600-3609.
Citation: https://doi.org/10.5194/acp-2022-205-RC1 - AC1: 'Reply on RC1', Kyung-Eun Min, 10 Jun 2022
-
RC2: 'Comment on acp-2022-205', Anonymous Referee #2, 02 May 2022
“Insights on estimating urban CO2 emissions using eddy-covariance flux measurements”Kyung-Eun Min et.al,2022
General comments
This manuscript try to quantify CO2 emission strengths of individual urban activities (vehicle, industry, heat generation et.al.) based on less than one year measurements with Eddy-Covariance (EC) method at Gwangju, Korea. The author estimated CO2 emission factors (EFs) of Traffic/Industry/Heat from the EC measurement, while the plants influence on CO2 exchange including photosynthesis and respiration can be estimated as the net balance of total emissions among all activities with observation (for the estimation of EF of vegetation). Based on their EFs estimations, they found that the annual CO2 emissions of traffic and space heating were more than 2.5 times higher than those of the emission inventory for Gwangju in 2017-2018. However, this experiment setup and data are not reliable. The CO2 flux measuring system was installed on the helideck of the Gwangju city hall, so the building’s effect on the EC measurement could not be ignoring. The results are not robust. On the other side, there are lot of EC towers to measurement the Co2 flux in city since the beginning of 21st century, and some sites have collected more than 10 years dataset. CO2 emission factors (EFs) could not be used to other city as a universal parameter for the estimation of the annual co2 flux in the city. By the way, the model to simulate the co2 flux over city has been published (Jarvi, L., et al.(2019),JGR: Atmos.).This manuscript is not suitable to be accepted by ACP.
Specific comments1) L123-134 “The CO2 flux measuring system was installed on the helideck of the Gwangju city hall,……Our EC system was installed outside of inertial sublayer …and sufficiently lower than the planetary boundary layer.”, is it correct? The measurement is set in Gwangju city hall (90 m above the ground – building height: 85 m, helideck: 3 m and measuring system structure: 2 m), so it is not satisfied the guideline on the flux measurement in the city. The building’s effect on the flow has large influence on the EC measurement.
2) L166-167 ”footprint boundaries were defined to confine 70% of average total flux during the measurement period.”, usually we use the footprint boundaries to cover 90% of average total flux “.
3) L172-174 ” To assess the quantitative contributions of the individual sources, the wind directions were split into two sectors; (1) the Eastern Industrial Area (EIA, 45º-100º) and (2) the Southern Green Area (SGA, 100º-225º), based on whether or not the fetch includes the automobile production plant and urban vegetation .“, it is too simple to assess the quantitative contributions with two sectors division, due to the complex flow field in this city. So the EFs estimation has too large uncertainty.
4) Gwangju city is located in a basin area, while there is a more than 1000m High Mountain in the east of the city. The local circulation due to the terrain should be occurred sometime during the season, and may be interaction with the urban heat island (UHI) in Gwangju city. So the co2 flux measurement may be also influence by the two circulation. This manuscript didn’t consider any information on the topic. By the way, the wind rose (daytime or nighttime) during 2017-2018 could not be found in the text.
Jarvi, L., Havu, M., Ward, H. C., Bellucco, V., McFadden, J. P., Toivonen, T., Heikinheimo, V., Kolari, P., Riikonen, A., & Grimmond, C. S. B. (2019). Spatial Modeling of Local-Scale Biogenic and Anthropogenic Carbon Dioxide Emissions in Helsinki. Journal of Geophysical Research : Atmospheres, 1124(15), 8363-8384.Citation: https://doi.org/10.5194/acp-2022-205-RC2 - AC2: 'Reply on RC2', Kyung-Eun Min, 10 Jun 2022
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