Please wait a minute...
Frontiers of Environmental Science & Engineering

ISSN 2095-2201

ISSN 2095-221X(Online)

CN 10-1013/X

Postal Subscription Code 80-973

2018 Impact Factor: 3.883

Front. Environ. Sci. Eng.    2022, Vol. 16 Issue (11) : 139    https://doi.org/10.1007/s11783-022-1574-z
RESEARCH ARTICLE
Variation characteristics of atmospheric methane and carbon dioxide in summertime at a coastal site in the South China Sea
Yangyan Cheng1,2,5, Ye Shan3, Yuhuan Xue1,2,5, Yujiao Zhu3, Xinfeng Wang3, Likun Xue3, Yanguang Liu4, Fangli Qiao1,2,5, Min Zhang1,2,5()
1. Key Laboratory of Marine Sciences and Numerical Modeling, First Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, China
2. Laboratory for Regional Oceanography and Numerical Modeling, Pilot National Laboratory for Marine Science and Technology (Qingdao), Qingdao 266237, China
3. Environment Research Institute, Shandong University, Qingdao 266237, China
4. Key Laboratory of Marine Geology and Metallogeny, First Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, China
5. Shandong Key Laboratory of Marine Sciences and Numerical Modeling, Qingdao 266061, China
 Download: PDF(13477 KB)   HTML
 Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks
Abstract

● Diurnal patterns of CH4 and CO2 are clearly extracted using EEMD.

● CH4 and CO2 show mid-morning high and evening low patterns during sea breezes.

● Wind direction significantly modulates the diurnal variations in CH4 and CO2.

Methane (CH4) and carbon dioxide (CO2) are the two most important greenhouse gases (GHGs). To examine the variation characteristics of CH4 and CO2 in the coastal South China Sea, atmospheric CH4 and CO2 measurements were performed in Bohe (BH), Guangdong, China, in summer 2021. By using an adaptive data analysis method, the diurnal patterns of CH4 and CO2 were clearly extracted and analysed in relation to the sea breeze (SB) and land breeze (LB), respectively. The average concentrations of CH4 and CO2 were 1876.91 ± 31.13 ppb and 407.99 ± 4.24 ppm during SB, and 1988.12 ± 109.92 ppb and 421.54 ± 14.89 ppm during LB, respectively. The values of CH4 and CO2 during SB basically coincided with the values and trends of marine background sites, showing that the BH station could serve as an ideal site for background GHG monitoring and dynamic analysis. The extracted diurnal variations in CH4 and CO2 showed sunrise high and sunset low patterns (with peaks at 5:00–7:00) during LB but mid-morning high and evening low patterns (with peaks at 9:00) during SB. The diurnal amplitude changes in both CH4 and CO2 during LB were almost two to three times those during SB. Wind direction significantly modulated the diurnal variations in CH4 and CO2. The results in this study provide a new way to examine the variations in GHGs on different timescales and can also help us gain a better understanding of GHG sources and distributions in the South China Sea.

Keywords Methane      Carbon dioxide      Diurnal pattern      Ensemble empirical mode decomposition      South China Sea      Sea breeze     
Corresponding Author(s): Min Zhang   
Issue Date: 29 May 2022
 Cite this article:   
Yangyan Cheng,Ye Shan,Yuhuan Xue, et al. Variation characteristics of atmospheric methane and carbon dioxide in summertime at a coastal site in the South China Sea[J]. Front. Environ. Sci. Eng., 2022, 16(11): 139.
 URL:  
https://academic.hep.com.cn/fese/EN/10.1007/s11783-022-1574-z
https://academic.hep.com.cn/fese/EN/Y2022/V16/I11/139
Fig.1  Main sampling devices in the sampling container.
Fig.2  (a) Time series of wind speed/direction, (b) air temperature and relative humidity, and (c) CH4 and (d) CO2 concentrations at the Bohe station in June 2021, segmented into Episode 1, Episode 2 and Episode 3 according to wind directions/speeds in June 2021. Data gaps of CH4 and CO2 during 12–15 June are interpolated, which are indicated by red lines (c, d).
Observational Site Observational Period CH4 (ppb) CO2 (ppm) Reference
Bohe, Guangdong, China Jun. 2021 (during SB) 1876.91 ± 31.13 407.99 ± 4.24 This study
Bohe, Guangdong, China Jun. 2021 (during LB) 1988.12 ± 109.92 421.54 ± 14.89 This study
Northern Yellow Sea and Bohai Strait, China Jul. 2011 1823.8–2020.7 / Zang et al., 2013
Northern Yellow Sea and Bohai Strait, China May. 2012 1887.2–2136.2 / Zang et al., 2013
Greenland, Denmark Jul. 2012 1720–1880 / Webster et al., 2015
Yongxing Island, China Dec. 2013–Nov. 2014 / 399.12–405.39 Lv et al., 2015
Yongxing Island, China Dec. 2013–Nov. 2017 1797–1944 / Jiang et al., 2021
Thumba, Thiruvananthapuram, India Jan. 2014–Aug. 2016 1989–2022 / Kavitha et al., 2018
Pudong, Shanghai, China Jun. 2017–May. 2018 2154 ± 190 428.36 ± 13.96 Wei et al., 2019
Mt. Waliguan, Qinghai, China 2019 1932.0 ± 0.1 / Liu et al., 2021
Mauna Loa, Hawaii, USA Sept. 2021 / 413.3 NOAA/ESRL and SIO
Global annual mean 2018 1869 ± 2 407.8 ± 0.1 World Meteorological Organization, 2019
Global annual mean 2019 1877 ± 2 410.5 ± 0.2 World Meteorological Organization, 2020
Tab.1  Comparison of atmospheric CH4 and CO2 concentrations with previous studies. (Note: The NOAA/ESRL and the SIO refer to the NOAA Earth System Research Laboratory and the Scripps Institution of Oceanography)
Fig.3  (a) Comparison of CH4 and (b) CO2 concentrations observed in this study (black line during sea breeze) with previous studies (coloured lines, dots and stars), with all values observed in June for each year. The greenhouse gas data at the Mauna Loa, Dongsha Island and Mt. Waliguan are from the website of the NOAA Global Monitoring Laboratory Carbon Cycle Cooperative Global Air Sampling Network. The CO2 data in Hok Tsui and King’s Park are updated online from the World Data Centre for Greenhouse Gases (WDCGG) website provided by the Hong Kong Observatory (China), while the CH4 data on Yongxing Island are from Jiang et al. (2021).
Fig.4  (a) Time series of atmospheric CH4 and (b) CO2 concentrations and (c) wind speed, with their extracted components from high-frequency to low-frequency using EEMD. (d) The IMF3s represent the diurnal fluctuations, segmented into Episode 1 (orange), Episode 2 (blue) and Episode 3 (green) at the Bohe station during June 2021.
Fig.5  Comparison of diurnal variations in (a) CH4, (b) CO2 and (c) wind speed as observed during Episode 1 (orange), Episode 2 (blue) and Episode 3 (green) at the Bohe station. Error bars indicate standard deviations with confidence intervals of 95%.
Fig.6  Scatterplots of CH4 and CO2 concentrations versus (a, b) wind speed, (c, d) TVOCs and (e, f) PM2.5 for three episodes. Fitted lines of the corresponding colored circles represent the ordinary least-squared regression model, and r is the Pearson Correlation Coefficient.
Fig.7  Bivariate polar plots for (a) CH4, (b) CO2, (c) PM2.5 and (d) TVOCs concentrations at the Bohe station in June 2021.
1 Y N Bai , X N Wang , F Zhang , R J Zeng . (2022). Acid Orange 7 degradation using methane as the sole carbon source and electron donor. Frontiers of Environmental Science & Engineering, 16( 3): 34
2 A R Brandt , G A Heath , E A Kort , F O’Sullivan , G Pétron , S M Jordaan , P Tans , J Wilcox , A M Gopstein , D Arent , S Wofsy , N J Brown , R Bradley , G D Stucky , D Eardley , R Harriss . (2014). Methane leaks from North American natural gas systems. Science, 343( 6172): 733– 735
https://doi.org/10.1126/science.1247045
3 W J Cai , M H Dai , Y C Wang . (2006). Air-sea exchange of carbon dioxide in ocean margins: A province-based synthesis. Geophysical Research Letters, 33( 12): L12603
https://doi.org/10.1029/2006GL026219
4 J G Canadell P M S Monteiro M H Costa L Cotrim da Cunha P M Cox A V Eliseev S Henson M Ishii S Jaccard C Koven. (2021). Global carbon and other biogeochemical cycles and feedbacks. In: Masson-Delmotte V, Zhai P, Pirani A, Connors S L, Péan C, Berger S, Caud N, Chen Y, Goldfarb L, Gomis M I, et al., eds. Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge: Cambridge University Press
5 D C Carslaw , S D Beevers . (2013). Characterising and understanding emission sources using bivariate polar plots and k-means clustering. Environmental Modelling & Software, 40 : 325– 329
6 X Y Chen , Y L Zhang , M Zhang , Y Feng , Z H Wu , F L Qiao , N E Huang . (2013). Intercomparison between observed and simulated variability in global ocean heat content using empirical mode decomposition, part I: modulated annual cycle. Climate Dynamics, 41( 11−12): 2797– 2815
https://doi.org/10.1007/s00382-012-1554-2
7 J Clow J C Smith ( 2016). Using Unmanned Air Systems to Monitor Methane in the Atmosphere. NASA/TM–2016–219008. Hampton: Langley Research Center
8 D Coumou , S Rahmstorf . (2012). A decade of weather extremes. Nature Climate Change, 2( 7): 491– 496
https://doi.org/10.1038/nclimate1452
9 N S Diffenbaugh , D Singh , J S Mankin , D E Horton , D L Swain , D Touma , A Charland , Y J Liu , M Haugen , M Tsiang , B Rajaratnam . (2016). Quantifying the influence of global warming on unprecedented extreme climate events. Proceedings of the National Academy of Sciences, 114( 19): 4881– 4886
10 K Dimitriou , A Bougiatioti , M Ramonet , F Pierros , P Michalopoulos , E Liakakou , S Solomos , P Y Quehe , M Delmotte , E Gerasopoulos . et al.. (2021). Greenhouse gases (CO2 and CH4) at an urban background site in Athens, Greece: Levels, sources and impact of atmospheric circulation. Atmospheric Environment, 253( 6): 118372
11 E J Dlugokencky E G Nisbet R Fisher D Lowry ( 2011). Global atmospheric methane: Budget, changes and dangers. Philosophical Transactions. Series A, Mathematical, physical, and engineering sciences, 369( 1943): 2058− 2072
12 S X Fang , P P Tans , B Yao , T Luan , Y L Wu , D J Yu . (2017). Study of atmospheric CO2 and CH4 at Longfengshan WMO/GAW regional station: The variations, trends, influence of local sources/sinks, and transport. Science China. Earth Sciences, 60( 10): 1886– 1895
https://doi.org/10.1007/s11430-016-9066-3
13 P Forster T Storelvmo K Armour W Collins J L Dufresne D Frame D J Lunt T Mauritsen M D Palmer M Watanabe. ( 2021). The Earth’s energy budget, climate feedbacks, and climate sensitivity. In: Masson-Delmotte V, Zhai P, Pirani A, Connors S L, Péan C, Berger S, Caud N, Chen Y, Goldfarb L, Gomis M I, et al., eds. Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovemmental Panel on Climate Change. Cambridge: Cambridge University Press
14 S K Gulev P W Thorne J Ahn F J Dentener C M Domingues S Gerland D Gong D S Kaufman H C Nnamchi J Quaas. ( 2021). Changing state of the climate system. In: Masson-Delmotte V, Zhai P, Pirani A, Connors S L, Péan C, Berger S, Caud N, Chen Y, Goldfarb L, Gomis M I, et al., eds. Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge: Cambridge University Press
15 J J He , M Zhang , X Y Chen , M Wang . (2014). Inter-comparison of seasonal variability and nonlinear trend between AERONET aerosol optical depth and PM10 mass concentrations in Hong Kong (China). Science China. Earth Sciences, 57( 11): 2606– 2615
https://doi.org/10.1007/s11430-014-4874-8
16 J Huang , P W Chan . (2011). Progress of marine meteorological observation experiment at Maoming of South China. Journal of Tropical Meteorology, 17( 4): 418– 429
17 N E Huang S Zheng S R Long M C Wu H H Shih Q Zheng N C Yen C C Tung H H (1998) Liu. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Philosophical Transactions. Series A, Mathematical, physical, and engineering sciences, 454( 1971): 903– 995
18 X L Ji , G M Liu , S Gao , H Wang , M Y Zhang . (2017). Comparison of air-sea CO2 flux and biological productivity in the South China Sea, East China Sea, and Yellow Sea: A three-dimensional physical-biogeochemical modeling study. Acta Oceanologica Sinica, 36( 12): 1– 10
https://doi.org/10.1007/s13131-017-1098-8
19 Y F Jiang X Q Wang H Y Wang Y S Xu H G (2021) Lv. Study on the concentration variation and impact factors of CH4 in Yongxing Island . China Environmental Science, 41(11): 5054− 5059 (in Chinese)
20 M Kavitha P R Nair I A Girach S Aneesh S Sijikumar R Renju ( 2018). Diurnal and seasonal variations in surface methane at a tropical coastal station: Role of mesoscale meteorology. Science of the Total Environment, 631– 632: 631– 632
21 S F Kong , B Lu , B Han , Z P Bai , Z Xu , Y You , L M Jin , X Y Guo , R Wang . (2010). Seasonal variation analysis of atmospheric CH4, N2O and CO2 in Tianjin offshore area. Science China. Earth Sciences, 53( 8): 1205– 1215
https://doi.org/10.1007/s11430-010-3065-5
22 H Y Li , Y J Zhu , Y Zhao , T S Chen , Y Jiang , Y Shan , Y H Liu , J S Mu , X K Yin , D Wu . et al.. (2020). Evaluation of the performance of low-cost air quality sensors at a high mountain station with complex meteorological conditions. Atmosphere, 11( 2): 212
https://doi.org/10.3390/atmos11020212
23 Q Li , S Gogo , F Leroy , C Guimbaud , F Laggoun-Défarge . (2021). Response of peatland CO2 and CH4 fluxes to experimental warming and the carbon balance. Frontiers of Earth Science, 9 : 631368
https://doi.org/10.3389/feart.2021.631368
24 S Liu , S X Fang , P Liu , M Liang , M R Guo , Z Z Feng . (2021). Measurement report: Changing characteristics of atmospheric CH4 in the Tibetan Plateau: Records from 1994 to 2019 at the Mount Waliguan station. Atmospheric Chemistry and Physics, 21( 1): 393– 413
https://doi.org/10.5194/acp-21-393-2021
25 H G Lv H Y Wang Y F Jiang H N Chen R Qiao Z G Wang ( 2015). Study on the concentration variation of CO2 in the background area of Xisha . Acta Oceanologica Sinica, 37(6): 21− 30 (in Chinese)
26 I A Pérez , M L Sánchez , M Á García , N Pardo . (2019). Sensitivity of CO2 and CH4 annual cycles to different meteorological variables at a rural site in Northern Spain. Advances in Meteorology, 2019 : 9240568
27 F L Qiao Y L Yuan J Deng D J Dai Z Y (2016) Song. Wave-turbulence interaction-induced vertical mixing and its effects in ocean and climate models. Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences, 374( 2065): 2065.201
28 T D Qu , Y T Song , T Yamagata . (2009). An introduction to the South China Sea throughflow: Its dynamics, variability, and application for climate. Dynamics of Atmospheres and Oceans, 47( 1−3): 3– 14
https://doi.org/10.1016/j.dynatmoce.2008.05.001
29 M Saunois , P Bousquet , B Poulter , A Peregon , P Ciais , J G Canadell , E J Dlugokencky , G Etiope , D Bastviken , S Houweling . et al.. (2016). The global methane budget 2000–2012. Earth System Science Data, 8 : 697– 751
https://doi.org/10.5194/essd-8-697-2016
30 S Srivastava , S Lal , D B Subrahamanyam , S Gupta , S Venkataramani , T A Rajesh . (2010). Seasonal variability in mixed layer height and its impact on trace gas distribution over a tropical urban site: Ahmedabad. Atmospheric Research, 96( 1): 79– 87
https://doi.org/10.1016/j.atmosres.2009.11.015
31 G Thomas , E J Zachariah . (2012). Ground level volume mixing ratio of methane in a tropical coastal city. Environmental Monitoring and Assessment, 184( 4): 1857– 1863
https://doi.org/10.1007/s10661-011-2084-9
32 Nations Environment Programme United ( 2021). Global Methane Assessment: Benefits and Costs of Mitigating Methane Emissions. Nairobi: United Nations Environment Programme
33 K D Webster J R White L M Pratt ( 2015). Ground-level concentrations of atmospheric methane in southwest Greenland evaluated using open-path laser spectroscopy and cavity-enhanced absorption spectroscopy. Arctic, Antarctic, and Alpine Research, 47( 4): 599− 609
34 C Wei , M H Wang , Q Y Fu , C Dai , R Huang , Q Bao . (2019). Temporal characteristics of greenhouse gases (CO2 and CH4) in the megacity Shanghai, China: Association with air pollutants and meteorological conditions. Atmospheric Research, 235 : 104759
35 Meteorological Organization World ( 2019). Greenhouse Gas Bulletin: The State of Greenhouse Gases in the Atmosphere Based on Global Observations through 2018. Geneva: World Meteorological Organization
36 Meteorological Organization World ( 2020). Greenhouse Gas Bulletin: The State of Greenhouse Gases in the Atmosphere Based on Global Observations through 2019. Geneva: World Meteorological Organization
37 Z H Wu , N E Huang . (2009). Ensemble empirical mode decomposition: A noise-assisted data analysis method. Advances in Adaptive Data Analysis, 1( 01): 1– 41
https://doi.org/10.1142/S1793536909000047
38 Z H Wu , E K Schneider , B P Kirtman , E S Sarachik , N E Huang , C J Tucker . (2008). The modulated annual cycle: An alternative reference frame for climate anomalies. Climate Dynamics, 31( 7): 823– 841
39 K P Zang H D Zhao J Y Wang X M Xu C Huo N Zheng ( 2013). High-resolution measurement of CH4 in sea surface air based on cavity ring-down spectroscopy technique: The first trial in China Seas . Acta Scientiae Circumstantiae, 33(5): 1362− 1366 (in Chinese)
40 K Zhang , J L Xu , Q Huang , L Zhou , Q Y Fu , Y S Duan , G L Xiu . (2020). Precursors and potential sources of ground-level ozone in suburban Shanghai. Frontiers of Environmental Science & Engineering, 14( 6): 92
41 M Zhang , Y Y Cheng , Y Bao , C Zhao , G Wang , Y L Zhang , Z Y Song , Z H Wu , F L Qiao . (2022). Seasonal to decadal spatiotemporal variations of the global ocean carbon sink. Global Change Biology, 28( 5): 1786– 1797
https://doi.org/10.1111/gcb.16031
42 M Zhang , F L Qiao , Z Y Song . (2017). Observation of atmospheric methane in the Arctic Ocean up to 87ºN. Science China. Earth Sciences, 60( 1): 173– 179
https://doi.org/10.1007/s11430-015-0241-3
43 M Zhang , Z H Wu , F L Qiao . (2018). Deep Atlantic Ocean warming facilitated by the deep western boundary current and equatorial Kelvin waves. Journal of Climate, 31( 20): 8541– 8555
https://doi.org/10.1175/JCLI-D-18-0255.1
44 Y Zhang , X Z Xiong , J H Tao , C Yu , M M Zou , L Su , L F Chen . (2014). Methane retrieval from atmospheric infrared sounder using EOF-based regression algorithm and its validation. Chinese Science Bulletin, 59( 14): 1508– 1518
https://doi.org/10.1007/s11434-014-0232-7
[1] Yan Guo, Zibin Luo, Junhao Shen, Yu-You Li. The main anammox-based processes, the involved microbes and the novel process concept from the application perspective[J]. Front. Environ. Sci. Eng., 2022, 16(7): 84-.
[2] Qiong Guo, Zhichao Yang, Bingliang Zhang, Ming Hua, Changhong Liu, Bingcai Pan. Enhanced methane production during long-term UASB operation at high organic loads as enabled by the immobilized Fungi[J]. Front. Environ. Sci. Eng., 2022, 16(6): 71-.
[3] He Zhao, Ching-Hua Huang, Chen Zhong, Penghui Du, Peizhe Sun. Enhanced formation of trihalomethane disinfection byproducts from halobenzoquinones under combined UV/chlorine conditions[J]. Front. Environ. Sci. Eng., 2022, 16(6): 76-.
[4] Xiaoyuan Zhang, Jun Gu, Shujuan Meng, Yu Liu. Dissolved methane in anaerobic effluent: Emission or recovery?[J]. Front. Environ. Sci. Eng., 2022, 16(4): 54-.
[5] Qinjun Liang, Yu Gao, Zhigang Li, Jiayi Cai, Na Chu, Wen Hao, Yong Jiang, Raymond Jianxiong Zeng. Electricity-driven ammonia oxidation and acetate production in microbial electrosynthesis systems[J]. Front. Environ. Sci. Eng., 2022, 16(4): 42-.
[6] Yanan Bai, Xiuning Wang, Fang Zhang, Raymond Jianxiong Zeng. Acid Orange 7 degradation using methane as the sole carbon source and electron donor[J]. Front. Environ. Sci. Eng., 2022, 16(3): 34-.
[7] Shaoyi Xu, Xiaolong Wu, Huijie Lu. Overlooked nitrogen-cycling microorganisms in biological wastewater treatment[J]. Front. Environ. Sci. Eng., 2021, 15(6): 133-.
[8] Ying Xu, Hui Gong, Xiaohu Dai. High-solid anaerobic digestion of sewage sludge: achievements and perspectives[J]. Front. Environ. Sci. Eng., 2021, 15(4): 71-.
[9] Mona Akbar, Muhammad Farooq Saleem Khan, Ling Qian, Hui Wang. Degradation of polyacrylamide (PAM) and methane production by mesophilic and thermophilic anaerobic digestion: Effect of temperature and concentration[J]. Front. Environ. Sci. Eng., 2020, 14(6): 98-.
[10] Chao Pang, Chunhua He, Zhenhu Hu, Shoujun Yuan, Wei Wang. Aggravation of membrane fouling and methane leakage by a three-phase separator in an external anaerobic ceramic membrane bioreactor[J]. Front. Environ. Sci. Eng., 2019, 13(4): 50-.
[11] Yuzhou Deng, Shengpan Peng, Haidi Liu, Shuangde Li, Yunfa Chen. Mechanism of dichloromethane disproportionation over mesoporous TiO2 under low temperature[J]. Front. Environ. Sci. Eng., 2019, 13(2): 21-.
[12] Alvyn P. Berg, Ting-An Fang, Hao L. Tang. Unlocked disinfection by-product formation potential upon exposure of swimming pool water to additional stimulants[J]. Front. Environ. Sci. Eng., 2019, 13(1): 10-.
[13] Yi Chen, Shilong He, Mengmeng Zhou, Tingting Pan, Yujia Xu, Yingxin Gao, Hengkang Wang. Feasibility assessment of up-flow anaerobic sludge blanket treatment of sulfamethoxazole pharmaceutical wastewater[J]. Front. Environ. Sci. Eng., 2018, 12(5): 13-.
[14] Xu Zhang, Huanhuan Yang, Xinlei Wang, Wen Song, Zhaojie Cui. An extraction- assay system: Evaluation on flavonols in plant resistance to Pb and Cd by supercritical extraction- gas chromatography[J]. Front. Environ. Sci. Eng., 2018, 12(4): 6-.
[15] Zechong Guo, Lei Gao, Ling Wang, Wenzong Liu, Aijie Wang. Enhanced methane recovery and exoelectrogen-methanogen evolution from low-strength wastewater in an up-flow biofilm reactor with conductive granular graphite fillers[J]. Front. Environ. Sci. Eng., 2018, 12(4): 13-.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed