the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
11 Mar 2021
11 Mar 2021
Spatial and temporal variations of CO2 mole fractions observed at Beijing, Xianghe and Xinglong in North China
- 1Shanghai Ecological Forecasting and Remote Sensing Center, Shanghai, China
- 2Key Laboratory of Middle Atmosphere and Global Environment Observation (LAGEO), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
- 3Royal Belgian Institute for Space Aeronomy, Brussels, Belgium
- 4State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry (LAPC), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
- 5State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
- 6The China Meteorological Administration, Meteorological Observation Centre
- 7University of Chinese Academy of Sciences, Beijing, China
- 1Shanghai Ecological Forecasting and Remote Sensing Center, Shanghai, China
- 2Key Laboratory of Middle Atmosphere and Global Environment Observation (LAGEO), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
- 3Royal Belgian Institute for Space Aeronomy, Brussels, Belgium
- 4State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry (LAPC), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
- 5State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
- 6The China Meteorological Administration, Meteorological Observation Centre
- 7University of Chinese Academy of Sciences, Beijing, China
Abstract. Atmospheric CO2 mole fractions are observed at Beijing (BJ), Xianghe (XH), and Xinglong (XL) in North China using the Picarro G2301 Cavity Ring-Down Spectroscopy instruments. The measurement system is described comprehensively for the first time. The geo-distances among these three sites are within 200 km, but they have very different surrounding environments: BJ is inside the megacity; XH is in the suburban area; XL is in the countryside on a mountain. The mean and standard deviation of CO2 mole fractions at BJ, XH, and XL between October 2018 and September 2019 are 448.4 ± 12.8 ppm, 436.0 ± 9.2 ppm and 420.6 ± 8.2 ppm, respectively. The seasonal variations of CO2 at these three sites are similar, with a maximum in winter and a minimum in summer, which is dominated by the terrestrial ecosystem. However, the seasonal variations of CO2 at BJ and XH are more affected by human activities as compared to XL. By using CO2 at XL as the background, CO2 enhancements are observed simultaneously at BJ and XH. The diurnal variations of CO2 are driven by the boundary layer height, photosynthesis and human activities at BJ, XH and XL. Moreover, we address the impact of the wind on the CO2 mole fractions at BJ and XL. This study provides an insight into the spatial and temporal variations of CO2 mole fractions in North China.
Yang Yang et al.
Status: open (until 06 May 2021)
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RC1: 'Comment on acp-2021-103', Anonymous Referee #3, 24 Mar 2021
reply
This is a carefully done study and the data is very valuable, but the preliminary data analysis and discussion have been done. What the main purpose of this study is? What’s the main influencing mechanism of CO2? are there some differences with other big cites or megaregions?
- Please explain the data processing method and the proportion of valid data at the three sites.
- As CO2 at XL is regarded as the background in this study, please explain whether there is a special data processing method for it, because the observational data at XL include not only the background information, but also local information about natural ecosystem and human activity, especially, the intake system of XL is on the roof.
- It is very pity that there are no meteorological parameters at XH. For the situation (2.1) and the meteorological field (2.3), it seems the air masses from BJ can be captured much more at XH because “the percentage of wind frequency in the north region is 34%, 36%, 50% and 60% respectively from spring to winter”. And the air masses can be captured at XL only when the wind comes from SW.
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RC2: 'Comment on acp-2021-103', Anonymous Referee #2, 04 Apr 2021
reply
This is an important and valuable data to assess temporal and spatial variations of CO2 in North China. However, this data must be further discussed in order to support the main reasons of those CO2 variations. Comparisons with other megacities will be a good approach to improve the discussions. Follow specific suggestions to help the authors improve descriptions and discussions of the manuscript:
Section 2.2.2: Further description of calibration and data processing:
1) How CRDS stability was checked over time, before and after malfunctions?
2) Describe the steps used during data processing; what kind of filters were used?
P5 – lines 101-102: Data filtering were not used to reduce uncertainties but to exclude no-valid data. Review this sentence.
Results and discussion
Section 3.1 time series: Strategies/methods to selection of background mole fractions must be further presented and discussed in order to show low influence of anthropogenic sources.
P7 - lines 138-144 : Please add mean (std) concentrations related to higher and low CO2 levels.
P7 – lines 149-150: Contribution of main sources (fossil fuel and heating) must be further discussed. Other sources as biomass burning from wildfires are important? If possible, trace gases/species would be used to identify activity of specific sources.
P9 – lines 166-167: Discuss the reasons of higher amplitudes in BJ.
P9 – lines 216-217: References must be added to support the assumption.
Section 3.4: Is important discuss CO2 variations with the wind direction based on local and remote sources.
Section 3.5: Reasons to CO2 mole fractions variations in L1 and L2 altitudes must be discussed.
Section 3.6: This section must be further assessed using different approaches. One of these strategies would be investigate seasonal differences during weekday-weekend.
P13 – line 255: Assumption of lowest anthropogenic emissions on Tuesday must be proven.
Yang Yang et al.
Yang Yang et al.
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