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.
Kyung-Eun Min et al.
Status: final response (author comments only)
RC1: 'Comment on acp-2022-205', Anonymous Referee #1, 26 Apr 2022
- AC1: 'Reply on RC1', Kyung-Eun Min, 10 Jun 2022
RC2: 'Comment on acp-2022-205', Anonymous Referee #2, 02 May 2022
- AC2: 'Reply on RC2', Kyung-Eun Min, 10 Jun 2022
Kyung-Eun Min et al.
Kyung-Eun Min et al.
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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.
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.
Baldocchi, D. 2014. Glob. Change Biol., 20, 3600-3609.