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Annual and seasonal cycles of CO2 and CH4 in a Mediterranean Spanish environment using different kernel functions

  • Beatriz Fernández-DuqueEmail author
  • Isidro A. Pérez
  • M. Ángeles García
  • Nuria Pardo
  • M. Luisa Sánchez
Original Paper
  • 189 Downloads

Abstract

This paper is based on CO2 and CH4 semi-hourly mole fraction measurements obtained at the Low Atmosphere Research Centre (CIB) between 2010 and 2016 using a Picarro G1301 analyser. The main aims of the study were to examine the temporal variation of CO2 and CH4 by using six different kernel functions, and to study the suitability of these functions to the dataset. The method used for the current study was based on experimental contour plots of R2 values in order to simultaneously determine the bandwidths of kernel functions for the long-term and short-term. An Epanechnikov, a Gaussian, a biweight, a triangular, a tricubic and a rectangular kernel function were applied to extract the salient features of both the long-term (trend) and the short-term (seasonality). The average linear increase growth rates found were mainly attributed to the terrestrial biosphere cycle and changes in the atmospheric circulation regime. The seasonal cycle exhibited a cyclical variation, revealing summer minima for both gases, which may be explained by a biological minimum. Kernel analysis showed two nocturnal CO2 maxima, in spring and autumn, linked to an increase in rainfall. For CO2 daytime records, only the spring peak was detected. As regards CH4, the maximum was located in winter. The best fit for the trend was obtained by the biweight kernel. In contrast, the best adjustment for seasonality was achieved from the Gaussian and the triangular kernel. To sum up, optimal bandwidth selection is important when kernel regression functions are employed. Since no important differences were found between the kernels employed, those which involve least computational effort are recommended.

Keywords

Trend Seasonal cycle Rural Kernel functions Bandwidth Contour plot 

Notes

Acknowledgements

The authors of this paper acknowledge financial support from the Spanish Ministry of Economy and Competitiveness and ERDF funds (Projects CGL2009-11979 and CGL2014-53948-P). The authors sincerely thank the editor and the two anonymous referees for their valuable comments and remarks.

References

  1. Artuso F, Chamard P, Piacentino S, Sferlazzo DM, De Silvestri L, di Sarra A, Meloni D, Monteleone F (2009) Influence of transport and trends in atmospheric CO2 at Lampedusa. Atmos Environ 43:3044–3051.  https://doi.org/10.1016/j.atmosenv.2009.03.027 CrossRefGoogle Scholar
  2. Barichivich J, Briffa KR, Myneni RB, Osborn TJ, Melvin TM, Ciais P, Piao S, Tucker C (2013) Large-scale variations in the vegetation growing season and annual cycle of atmospheric CO2 at high northern latitudes from 1950 to 2011. Glob Change Biol 19:3167–3183.  https://doi.org/10.1111/gcb.12283 CrossRefGoogle Scholar
  3. Barlow JM, Palmer PI, Bruhwiler LM, Tans P (2015) Analysis of CO2 mole fraction data: first evidence of large-scale changes in CO2 uptake at high northern latitudes. Atmos Chem Phys 15:13739–13758.  https://doi.org/10.5194/acp-15-13739-2015 CrossRefGoogle Scholar
  4. Blake DR, Mayer EW, Tyler SC, Makide Y, Montague DC, Rowland FS (1982) Global increase in atmospheric methane concentrations between 1978 and 1980. Geophys Res Lett 9:477–480.  https://doi.org/10.1029/GL009i004p00477 CrossRefGoogle Scholar
  5. Casas G (2010) Estimaciones vía kernel. https://es.scribd.com/doc/57531637/Estimaciones-via-Kernel. Accessed 5 May 17
  6. Crosson ER (2008) A cavity ring-down analyzer for measuring atmospheric levels of methane, carbon dioxide, and water vapor. Appl Phys B 92:403–408.  https://doi.org/10.1007/s00340-008-3135-y CrossRefGoogle Scholar
  7. de Haan P (1999) On the use of density kernels for concentration estimations within particle and puff dispersion models. Atmos Environ 33:2007–2021.  https://doi.org/10.1016/S1352-2310(98)00424-5 CrossRefGoogle Scholar
  8. DeCarlo LT (1997) On the meaning and use of kurtosis. Psychol Methods 2:292–307CrossRefGoogle Scholar
  9. Dlugokencky EJ, Steele LP, Lang PM, Masarie A (1994) The growth rate and distribution of atmospheric methane. J Geophys Res 99:17021–17043.  https://doi.org/10.1029/94JD01245 CrossRefGoogle Scholar
  10. Donnelly A, Misstear B, Broderick B (2011) Application of nonparametric regression methods to study the relationship between NO2 concentrations and local wind direction and speed at background sites. Sci Total Environ 409(6):1134–1144.  https://doi.org/10.1016/j.scitotenv.2010.12.001 CrossRefGoogle Scholar
  11. Donnelly A, Broderick B, Misstear B (2012) Relating background NO2 concentrations in air to air mass history using non-parametric regression methods: application at two background sites in Ireland. Environ Model Assess 17:363–373.  https://doi.org/10.1007/s10666-011-9301-3 CrossRefGoogle Scholar
  12. Fang SX, Tans PP, Dong F, Zhou H, Luan T (2016) Characteristics of atmospheric CO2 and CH4 at the Shangdianzi regional background station in China. Atmos Environ 131:1–8.  https://doi.org/10.1016/j.atmosenv.2016.01.044 CrossRefGoogle Scholar
  13. Fernández-Duque B, Pérez IA, Sánchez ML, García MÁ, Pardo N (2017) Temporal patterns of CO2 and CH4 in a rural area in northern Spain described by a harmonic equation over 2010-2016. Sci Total Environ 593–594:1–9.  https://doi.org/10.1016/j.scitotenv.2017.03.132 CrossRefGoogle Scholar
  14. García MÁ, Sánchez ML, Pérez IA, Ozores MI, Pardo N (2016) Influence of atmospheric stability and transport on CH4 concentrations in northern Spain. Sci Total Environ 550:157–166.  https://doi.org/10.1016/j.scitotenv.2016.01.099 CrossRefGoogle Scholar
  15. García-Portugués E, Barros AMG, Crujeiras RM, González-Manteiga W, Pereira J (2014) A test for directional-linear independence, with applications to wildfire orientation and size. Stoch Environ Res Risk Assess 28:1261–1275.  https://doi.org/10.1007/s00477-013-0819-6 CrossRefGoogle Scholar
  16. Grange SK, Lewis AC, Carslaw DC (2016) Source apportionment advances using polar plots of bivariate correlation and regression statistics. Atmos Environ 145:128–134.  https://doi.org/10.1016/j.atmosenv.2016.09.016 CrossRefGoogle Scholar
  17. Graven HD, Guilderson TP, Keeling RF (2012) Observations of radiocarbon in CO2 at La Jolla, California, USA 1992–2007: analysis of the long-term trend. J Geophys Res Atmos 117:D02302.  https://doi.org/10.1029/2011JD016533 Google Scholar
  18. Gutiérrez R, Gutiérrez-Sánchez R, Nafidi A (2008) Trend analysis using nonhomogeneous stochastic diffusion processes. Emission of CO2; Kyoto protocol in Spain. Stoch Environ Res Risk Assess 22:57–66.  https://doi.org/10.1007/s00477-006-0097-7 CrossRefGoogle Scholar
  19. Hall P, Kang KH (2005) Bandwidth choice for nonparametric classification. Ann Stat 33:284–306.  https://doi.org/10.1214/009053604000000959 CrossRefGoogle Scholar
  20. Härdle W (1993) Applied nonparametric regression. Cambridge University Press, CambridgeGoogle Scholar
  21. Harrold TI, Sharma A, Sheather S (2001) Selection of a kernel bandwidth for measuring dependence in hydrologic time series using the mutual information criterion. Stoch Environ Res Risk Assess 15:310–324.  https://doi.org/10.1007/s004770100073 CrossRefGoogle Scholar
  22. Hatakka J, Aalto T, Aaltonen V, Aurela M, Hakola H, Komppula M, Laurila T, Lihavainen H, Paatero J, Salminen K, Viisanen Y (2003) Overview of the atmospheric research activities and results at Pallas GAW station. Boreal Environ Res 8:365–383Google Scholar
  23. Henry RC (2008) Locating and quantifying the impact of local sources of air pollution. Atmos Environ 42:358–363.  https://doi.org/10.1016/j.atmosenv.2007.09.039 CrossRefGoogle Scholar
  24. Henry RC, Chang YS, Spiegelman CH (2002) Locating nearby sources of air pollution by nonparametric regression of atmospheric concentrations on wind direction. Atmos Environ 36:2237–2244.  https://doi.org/10.1016/S1352-2310(02)00164-4 CrossRefGoogle Scholar
  25. Henry R, Norris GA, Vedantham R, Turner JR (2009) Source region identification using kernel smoothing. Environ Sci Technol 43:4090–4097.  https://doi.org/10.1021/es8011723 CrossRefGoogle Scholar
  26. Henry RC, Vette A, Norris G, Vedantham R, Kimbrough S, Shores RC (2011) Separating the air quality impact of a major highway and nearby sources by nonparametric trajectory analysis. Environ Sci Technol 45:10471–10476.  https://doi.org/10.1021/es202070k CrossRefGoogle Scholar
  27. IPCC (2013) Summary for Policymakers. In: Stocker TF, Qin D, Plattner GK, Tignor M, Allen SK, Boschung J, Nauels A, Xia Y, Bex V, Midgley PM (eds.) Climate change 2013: the physical science basis. Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change. Cambridge University Press: Cambridge, p 29Google Scholar
  28. IPCC (2014) Climate change 2014: mitigation of climate change. In: Edenhofer O, Pichs-Madruga R, Sokona Y, Farahani E, Kadner S, Seyboth K, Adler A, Baum I, Brunner S, Eickemeier P, Kriemann B, Savolainen J, Schlömer S, von Stechow V, Zwickel T, Minx JC (eds.) Contribution of working group III to the fifth assessment report of the intergovernmental panel on climate change. Cambridge University Press: Cambridge, p 1435Google Scholar
  29. Keeling CD (1960) The concentration and isotopic abundances of carbon dioxide in the atmosphere. Tellus 12:200–203.  https://doi.org/10.1111/j.2153-3490.1960.tb01300.x CrossRefGoogle Scholar
  30. Khurshid A, Hussain E, Ul-Haq M (2007) A note on finding peakedness in bivariate normal distribution using Mathematica. Pak J Stat Oper Res 3:75–86.  https://doi.org/10.18187/pjsor.v3i2.61 CrossRefGoogle Scholar
  31. Kim E, Hopke PK (2004) Comparison between conditional probability function and nonparametric regression for fine particle source directions. Atmos Environ 38:4667–4673.  https://doi.org/10.1016/j.atmosenv.2004.05.035 CrossRefGoogle Scholar
  32. Kim HS, Chung YS, Tans PP, Dlugokencky EJ (2015) Decadal trends of atmospheric methane in East Asia from 1991 to 2013. Air Qual Atmos Health 8:293–298.  https://doi.org/10.1007/s11869-015-0331-x CrossRefGoogle Scholar
  33. Le Quéré C, Andrew RM, Friedlingstein P, Sitch S, Pongratz J, Manning AC, Korsbakken JI, Peters GP, Canadell JG, Jackson RB, Boden TA, Tans PP, Andrews OD, Arora VK, Bakker DCE, Barbero L, Becker M, Betts RA, Bopp L, Chevallier F, Chini LP, Ciais P, Cosca CE, Cross J, Currie K, Gasser T, Harris I, Hauck J, Haverd V, Houghton RA, Hunt CW, Hurtt G, Ilyina T, Jain AK, Kato E, Kautz M, Keeling RF, Klein Goldewijk K, Körtzinger A, Landschützer P, Lefèvre N, Lenton A, Lienert S, Lima I, Lombardozzi D, Metzl N, Millero F, Monteiro PMS, Munro DR, Nabel JEMS, Nakaoka S-I, Nojiri Y, Padin XA, Peregon A, Pfeil B, Pierrot D, Poulter B, Rehder G, Reimer J, Rödenbeck C, Schwinger J, Séférian R, Skjelvan I, Stocker BD, Tian H, Tilbrook B, Tubiello FN, van der Laan-Luijkx IT, van der Werf GR, van Heuven S, Viovy N, Vuichard N, Walker AP, Watson AJ, Wiltshire AJ, Zaehle S, Zhu D (2018) Global Carbon Budget 2017. Earth Syst Sci Data 10:405–448.  https://doi.org/10.5194/essd-10-405-2018 CrossRefGoogle Scholar
  34. Nakazawa T, Ishizawa M, Higuchi K, Trivett NBA (1997) Two curve fitting methods applied to CO2 flask data. Environmetrics 8:197–218. https://doi.org/10.1002/(SICI)1099-095X(199705)8:3<197::AID-ENV248>3.0.CO;2-C
  35. Nicolich M, Jorgensen G (2008) Graphical presentation of a nonparametric regression with bootstrapped confidence intervals. http://103.28.21.22/Record/IOS10-oai:CiteSeerX.psu:10.1.1.124.889. Accessed 30 April 2017
  36. Nisbet EG, Dlugokencky EJ, Bousquet P (2014) Methane on the rise—again. Atmos Sci 343:493–495.  https://doi.org/10.1126/science.1247828 Google Scholar
  37. NOAA (2017a) CO2 globally averaged marine surface monthly mean data. ftp://aftp.cmdl.noaa.gov/products/trends/co2/co2_mm_gl.txt. Accessed 25 April 2017
  38. NOAA (2017b) CH4 globally averaged marine surface monthly mean data. ftp://aftp.cmdl.noaa.gov/products/trends/ch4/ch4_mm_gl.txt. Accessed 25 April 2017
  39. NOAA (2017c) Annual Mean Global Carbon Dioxide Growth Rates https://www.esrl.noaa.gov/gmd/ccgg/trends/gl_gr.html. Accessed 15 May 2018
  40. NOAA (2017d) Annual Increase in Globally-Averaged Atmospheric Methane. https://www.esrl.noaa.gov/gmd/ccgg/trends_ch4/#global_growth. Accessed 15 May 2018
  41. Pérez IA, Sánchez ML, García MÁ, Pardo N (2012) Analysis of CO2 daily cycle in the low atmosphere at a rural site. Sci Total Environ 431:286–292.  https://doi.org/10.1016/j.scitotenv.2012.05.067 CrossRefGoogle Scholar
  42. Pérez IA, Sánchez ML, García MÁ, Pardo N (2013) Carbon dioxide at an unpolluted site analysed with the smoothing kernel method and skewed distributions. Sci Total Environ 456–457:239–245.  https://doi.org/10.1016/j.scitotenv.2013.03.075 CrossRefGoogle Scholar
  43. Pérez IA, Sánchez ML, García MÁ, Pardo N (2015) Daily patterns of CO2 in the lower atmosphere of a rural site. Theor Appl Climatol 122:195–205.  https://doi.org/10.1007/s00704-014-1294-9 CrossRefGoogle Scholar
  44. Pérez IA, Sánchez ML, García MÁ, Pardo N (2016) Features of the annual evolution of CO2 and CH4 in the atmosphere of a Mediterranean climate site studied using a nonparametric and a harmonic function. Atmos Pollut Res 7:1013–1021.  https://doi.org/10.1016/j.apr.2016.06.006 CrossRefGoogle Scholar
  45. Pérez IA, Sánchez ML, García MÁ, Pardo N (2017) Trend analysis of CO2 and CH4 recorded at a semi-natural site in the northern plateau of the Iberian Peninsula. Atmos Environ 151:24–33.  https://doi.org/10.1016/j.atmosenv.2016.11.068 CrossRefGoogle Scholar
  46. Piao S, Liu Z, Wang Y, Ciais P, Yao Y, Peng S, Chevallier F, Friedlingstein P, Janssens IA, Peñuelas J, Sitch S, Wang T (2017) On the causes of trends in the seasonal amplitude of atmospheric CO2. Glob Chang Biol 24(2):608–616.  https://doi.org/10.1111/gcb.13909 CrossRefGoogle Scholar
  47. Pickers PA, Manning AC (2015) Investigating bias in the application of curve fitting programs to atmospheric time series. Atmos Meas Tech 8:1469–1489.  https://doi.org/10.5194/amt-8-1469-2015 CrossRefGoogle Scholar
  48. Rasmussen R, Khalil M (1981) Atmospheric methane (CH4): trends and seasonal cycles. J Geophys Res 86:9826–9832.  https://doi.org/10.1029/JC086iC10p09826 CrossRefGoogle Scholar
  49. Rella CW, Chen H, Andrews AE, Filges A, Gerbig C, Hatakka J, Karion A, Miles NL, Richardson SJ, Steinbacher M, Sweeney C, Wastine B, Zellweger C (2013) High accuracy measurements of dry mole fractions of carbon dioxide and methane in humid air. Atmos Meas Tech 6:837–860.  https://doi.org/10.5194/amt-6-837-2013 CrossRefGoogle Scholar
  50. Rodríguez-Cortés FJ, Mateu J (2015) Second-order smoothing of spatial point patterns with small sample sizes: a family of kernels. Stoch Environ Res Risk Assess 29:295–308.  https://doi.org/10.1007/s00477-014-0944-x CrossRefGoogle Scholar
  51. Sachs L (1978) Applied statistics: a handbook of techniques, 5th edn. Springer, New YorkGoogle Scholar
  52. Sánchez ML, García MA, Pérez IA, Pardo N (2014) CH4 continuous measurements in the upper Spanish plateau. Environ Monit Assess 186:2823–2834.  https://doi.org/10.1007/s10661-013-3583-7 CrossRefGoogle Scholar
  53. Saunois M, Bousquet P, Poulter B, Peregon A, Ciais P, Canadell JG, Dlugokencky EJ, Etiope G, Bastviken D, Houweling S, Janssens-Maenhout G, Tubiello FN, Castaldi S, Jackson RB, Alexe M, Arora VK, Beerling DJ, Bergamaschi P, Blake DR, Brailsford G, Brovkin V, Bruhwiler L, Crevoisier C, Crill P, Covey K, Curry C, Frankenberg C, Gedney N, Höglund-Isaksson L, Ishizawa M, Ito A, Joos F, Kim H-S, Kleinen T, Krummel P, Lamarque J-F, Langenfelds R, Locatelli R, Machida T, Maksyutov S, McDonald KC, Marshall J, Melton JR, Morino I, Naik V, O’Doherty S, Parmentier F-JW, Patra PK, Peng C, Peng S, Peters GP, Pison I, Prigent C, Prinn R, Ramonet M, Riley WJ, Saito M, Santini M, Schroeder R, Simpson IJ, Spahni R, Steele P, Takizawa A, Thornton BF, Tian H, Tohjima Y, Viovy N, Voulgarakis A, van Weele M, van der Werf GR, Weiss R, Wiedinmyer C, Wilton DJ, Wiltshire A, Worthy D, Wunch D, Xu X, Yoshida Y, Zhang B, Zhang Z, Zhu Q (2016) The global methane budget 2000–2012. Earth Syst Sci Data 8:697–751.  https://doi.org/10.5194/essd-8-697-2016 CrossRefGoogle Scholar
  54. Scott DW (1992) Multivariate density estimation: theory, practice, and visualization. Wiley, New YorkCrossRefGoogle Scholar
  55. Silverman BW (1998) Density estimation for statistics and data analysis. Chapman & Hall/CRC, Boca RatonGoogle Scholar
  56. Vermeulen AT, Hensen A, Popa ME, van den Bulk WCM, Jongejan PAC (2011) Greenhouse gas observations from Cabauw Tall Tower (1992–2010). Atmos Meas Tech 4:617–644.  https://doi.org/10.5194/amt-4-617-2011 CrossRefGoogle Scholar
  57. Wilks DS (2006) Statistical methods in the atmospheric sciences, 2nd edn. Elsevier, AmsterdamGoogle Scholar
  58. Wu J, Guan D, Yuan F, Yang H, Wang A, Jin C (2012) Evolution of atmospheric carbon dioxide concentration at different temporal scales recorded in a tall forest. Atmos Environ 61:9–14.  https://doi.org/10.1016/j.atmosenv.2012.07.013 CrossRefGoogle Scholar
  59. Yu KN, Cheung YP, Cheung T, Henry RC (2004) Identifying the impact of large urban airports on local air quality by nonparametric regression. Atmos Environ 38:4501–4507.  https://doi.org/10.1016/j.atmosenv.2004.05.034 CrossRefGoogle Scholar
  60. Zhang D, Tang J, Shi G, Nakazawa T, Aoki S, Sugawara S, Wen M, Morimoto S, Patra PK, Hayasaka T, Saeki T (2008) Temporal and spatial variations of the atmospheric CO2 concentration in China. Geophys Res Lett 35:L03801.  https://doi.org/10.1029/2007GL032531 Google Scholar
  61. Zhu C, Yoshikawa-Inoue H (2015) Seven years of observational atmospheric CO2 at a maritime site in northernmost Japan and its implications. Sci Total Environ 524–525:331–337.  https://doi.org/10.1016/j.scitotenv.2015.04.044 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Beatriz Fernández-Duque
    • 1
    Email author
  • Isidro A. Pérez
    • 1
  • M. Ángeles García
    • 1
  • Nuria Pardo
    • 1
  • M. Luisa Sánchez
    • 1
  1. 1.Department of Applied Physics. Faculty of SciencesUniversity of ValladolidValladolidSpain

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