Boundary-Layer Meteorology

, Volume 161, Issue 2, pp 237–264 | Cite as

Cluster Analysis of Atmospheric Dynamics and Pollution Transport in a Coastal Area

  • Anton Sokolov
  • Egor Dmitriev
  • Elena Maksimovich
  • Hervé Delbarre
  • Patrick Augustin
  • Cyril Gengembre
  • Marc Fourmentin
  • Nadine Locoge
Research Article


Summertime atmospheric dynamics in the coastal zone of the industrialized Dunkerque agglomeration in northern France was characterized by a cluster analysis of back trajectories in the context of pollution transport. The MESO-NH atmospheric model was used to simulate the local dynamics at multiple scales with horizontal resolution down to 500 m, and for the online calculation of the Lagrangian backward trajectories with 30-min temporal resolution. Airmass transport was performed along six principal pathways obtained by the weighted k-means clustering technique. Four of these centroids corresponded to a range of wind speeds over the English Channel: two for wind directions from the north-east and two from the south-west. Another pathway corresponded to a south-westerly continental transport. The backward trajectories of the largest and most dispersed sixth cluster contained low wind speeds, including sea-breeze circulations. Based on analyses of meteorological data and pollution measurements, the principal atmospheric pathways were related to local air-contamination events. Continuous air quality and meteorological data were collected during the Benzene–Toluene–Ethylbenzene–Xylene 2006 campaign. The sites of the pollution measurements served as the endpoints for the backward trajectories. Pollutant transport pathways corresponding to the highest air contamination were defined.


Atmospheric dynamics Atmospheric pollution Backward trajectories Cluster analysis Mesoscale modelling 



We gratefully acknowledge the financial support received from IRENI (Institut de Recherche en Environnement Industriel) and CAPPA (Chemical and Physical Properties of the Atmosphere) that is funded by the French National Research Agency (ANR) through PIA (Programme d’Investissement d’Avenir) under the contract ANR-10-LABX-005.

Compliance with Ethical Standards

Conflicts of interest

The authors declare that they have no conflict of interest.


  1. Adame JA, Notario A, Villanueva F, Albaladejo J (2012) Application of cluster analysis to surface ozone, NO\(_2\) and SO\(_2\) daily patterns in an industrial area in Central-Southern Spain measured with a DOAS system. Sci Total Environ 429:281–291CrossRefGoogle Scholar
  2. Atkinson R (1990) Gas-phase tropospheric chemistry of organic compounds. Atmos Environ 24A:1–41CrossRefGoogle Scholar
  3. Atkinson R, Arey J (2003) Atmospheric degradation of volatile organic compounds. Chem Rev 103(12):4605–4638CrossRefGoogle Scholar
  4. Augustin P, Delbarre H, Lohou F, Campistron B, Puygrenier V, Cachier H, Lombardo T (2006) Investigation of local meteorological events and their relationship with ozone and aerosols during an ESCOMPTE photochemical episode. Ann Geophys 24:2809–2822CrossRefGoogle Scholar
  5. Barbakh W, Fyfe C (2008) Online clustering algorithms. Int J Neural Syst 18(3):1–10CrossRefGoogle Scholar
  6. Bechtold P, Bazile E, Guichard F, Mascart P, Richard E (2001) A mass flux convection scheme for regional and global models. Q J R Meteorol Soc 127:869–886CrossRefGoogle Scholar
  7. Berrisford P, Dee DP, Fielding K, Fuentes M, Kållberg P, Kobayashi S, Uppala SM (2009) The ERA-interim archive. ERA report series, No. 1. ECMWF, ReadingGoogle Scholar
  8. Bottou L, Bengio Y (1995) Convergence properties of the k-means algorithms. Advances in neural information processing systems. MIT Press, CambridgeGoogle Scholar
  9. Bougeault P, Lacarrère P (1989) Parameterization of orography-induced turbulence in a meso-beta scale model. Mon Weather Rev 117:1872–1890CrossRefGoogle Scholar
  10. Cuxart J, Bougeault P, Redelsperger JL (2000) A turbulence scheme allowing for mesoscale and large-eddy simulations. Q J R Meteorol Soc 126:1–30CrossRefGoogle Scholar
  11. Damato F, Planchon O, Dubreuil V (2003) A remote-sensing study of the inland penetration of sea-breeze fronts from the English Channel. Weather 58:219–226CrossRefGoogle Scholar
  12. Draxler R, Hess G (1998) An overview of the HYSPLIT_4 modelling system of trajectories, dispersion, and deposition. Aust Meteorol Mag 47:295–308Google Scholar
  13. DRIRE IRE (2003) Direction Régionale de l’Industrie, de la Recherche et de l’Environnement Nord - Pas-de-Calais, l’Industrie au Regard de l’EnvironnementGoogle Scholar
  14. DRIRE IRE (2009) Direction Régionale de l’Industrie, de la Recherche et de l’Environnement Nord - Pas-de-Calais, l’Industrie au Regard de l’EnvironnementGoogle Scholar
  15. Faroux S, Kaptué Tchuenté AT, Roujean JL, Masson V, Martin E, Le Moigne P (2013) ECOCLIMAP-II/Europe: a twofold database of ecosystems and surface parameters at 1 km resolution based on satellite information for use in land surface, meteorological and climate models. Geosci Model Dev 6:563–582CrossRefGoogle Scholar
  16. Filella I, Peñuelas J (2006) Daily, weekly, and seasonal time courses of VOC concentrations in a semi-urban area near Barcelona. Atmos Environ 40:7752–7769CrossRefGoogle Scholar
  17. Fischer C, Auger L (2011) Some experimental lessons on digital filtering in the ALADIN-France 3DVAR based on near-ground examination. Mon Weather Rev 139:774–785CrossRefGoogle Scholar
  18. Gheusi F, Stein J (2002) Lagrangian description of air flows using Eulerian passive tracers. Q J R Meteorol Soc 128:337–360CrossRefGoogle Scholar
  19. Grell GA, Dudhia J, Stauffer D (1994) A description of the fifth generation Penn State/NCAR Meso-scale Model (MM5). NCAR Technical Note, NCAR TN-398- STRGoogle Scholar
  20. IARC (2007) International agency for research on cancer, monographs on the evaluation of carcinogenic risks to humans. IARC Monogr, vol 89, 641 pp.
  21. Jabouille P, Guivarch R, Kloos P, Gazen D, Gicquel N, Giraud L, Asencio N, Ducrocq V, Escobar J, Redelsperger JL, Stein J, Pinty JP (1999) Parallelization of the French meteorological mesoscale model MésoNH, vol 1685. Lecture Notes in Computer ScienceSpringer, Berlin, pp 1417–1422Google Scholar
  22. Jorba O, Pérez C, Rocadenbosch F, Baldasano JM (2004) Cluster analysis of 4-day back trajectories arriving in the Barcelona area, Spain, from 1997 to 2002. J Appl Meteorol Climatol 43:887–901CrossRefGoogle Scholar
  23. Kaufman L, Rousseeuw PJ (1990) Finding groups in data: an introduction to cluster analysis. John Wiley & Sons Inc, Hoboken, 342 ppGoogle Scholar
  24. Kraft M, Eikmann T, Kappos A, Künzli N, Rapp R, Schneider K, Seitz H, Voss J, Wichmann H (2005) The German view: effects of nitrogen dioxide on human health—derivation of health-related short-term and long-term values. Int J Hyg Environ Health 208:305–318CrossRefGoogle Scholar
  25. Lafore JP, Stein J, Asencio N, Bougeault P, Ducrocq V, Duron J, Fischer C, Héreil P, Mascart P, Masson V, Pinty JP, Redelsperger JL, Richard E, Vilà-Guerau de Arellano J (1998) The MESO-NH atmospheric simulation system. Part I: adiabatic formulation and control simulations. Scientific objectives and experimental design. Ann Geophys 16:90–109CrossRefGoogle Scholar
  26. Lauwerys R, Lison D (2007) Toxicologie industrielle et intoxications professionnelles, 5-ième ed. Masson, Paris, p 1268Google Scholar
  27. Leroy C (2008) Analyse dynamique de la pollution de l’air dans la troposphère. PhD thesis. 264 ppGoogle Scholar
  28. Liu N, Yu Y, He J, Zhao S (2013) Integrated modelling of urban-scale pollutant transport: application in a semi-arid urban valley, Northwestern China. Atmos Pollut Res 4:306–314CrossRefGoogle Scholar
  29. Mace A, Sommariva R, Fleming Z, Wang W (2011) Adaptive K-means for clustering air mass trajectories. Intelligent data engineering and automated learning—IDEAL 2011, vol 6936. Lecture Notes in Computer ScienceSpringer, Berlin, pp 1–8Google Scholar
  30. MacQueen JB (1967) Some methods for classification and analysis of multivariate observations. In: Proceedings of 5th Berkeley symposium on mathematical statistics and probability. Univ. of California Press, pp 281–297Google Scholar
  31. Markou MT, Kassomenos P (2010) Cluster analysis of 5 years of back trajectories arriving in Athens, Greece. Atmos Res 98:438–457CrossRefGoogle Scholar
  32. Masson V, Le Moigne P, Martin E, Faroux S, Alias A, Alkama R, Belamari S, Barbu A, Boone A, Bouyssel F, Brousseau P, Brun E, Calvet JC, Carrer D, Decharme B, Delire C, Donier S, Essaouini K, Gibelin AL, Giordani H, Habets F, Jidane M, Kerdraon G, Kourzeneva E, Lafaysse M, Lafont S, Lebeaupin Brossier C, Lemonsu A, Mahfouf JF, Marguinaud P, Mokhtari M, Morin S, Pigeon G, Salgado R, Seity Y, Taillefer F, Tanguy G, Tulet P, Vincendon B, Vionnet V, Voldoire A (2013) The SURFEXv7.2 land and ocean surface platform for coupled or offline simulation of earth surface variables and fluxes. Geosci Model Dev 6:929–960CrossRefGoogle Scholar
  33. Masson V, Champeaux JL, Chauvin C, Meriguet C, Lacaze R (2003) A global database of land surface parameters at 1 km resolution for use in meteorological and climate models. J Clim 16:1261–1282CrossRefGoogle Scholar
  34. Marris H, Deboudt K, Augustin P, Flament P, Blond F, Fiani E, Fourmentin M, Delbarre H (2012) Fast changes in particle composition and size during the near-field transport of industrial plume. Sci Total Environ 427–428:126–138CrossRefGoogle Scholar
  35. MESO-NH non-hydrostatic mesoscale atmospheric model web site:
  36. Miltenberger AK, Pfahl S, Wernli H (2013) An online trajectory module (version 1.0) for the nonhydrostatic numerical weather prediction model COSMO. Geosci Model Dev 6:1989–2004CrossRefGoogle Scholar
  37. Miller STK, Keim BD, Talbot RW, Mao H (2003) Sea breeze: structure, forecasting and impacts. Rev Geophys 41:3.1–3.37CrossRefGoogle Scholar
  38. Mlawer EJ, Taubman SJ, Brown PD, Iacono MJ, Clough SA (1997) Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave. J Geophys Res 102:16663–16682CrossRefGoogle Scholar
  39. Morcrette JJ (1991) Radiation and cloud radiative properties in the European center for medium range weather forecasts forecasting system. J Geophys Res 96:9121–9132CrossRefGoogle Scholar
  40. Na K, Kim YP, Moon KC (2003) Diurnal characteristics of volatile organic, compounds in the Seoul atmosphere. Atmos Environ 37:733–742CrossRefGoogle Scholar
  41. Noilhan J, Planton S (1989) A simple parameterization of land surface processes for meteorological models. Mon Weather Rev 117:536–549CrossRefGoogle Scholar
  42. Rimetz-Planchon J, Perdrix E, Sobanska S, Brémard C (2008) PM10 air quality variations in an urbanized and industrialized harbor. Atmos Environ 42(31):7274–7283CrossRefGoogle Scholar
  43. Roukos J, Riffault V, Locoge N, Plaisance H (2009) VOC in an urban and industrial harbor on the French North Sea coast during two contrasted meteorological situations. Environ Pollut 157(11):3001–3009CrossRefGoogle Scholar
  44. Seco R, Peñuelas J, Filella I, Llusia J, Schallhart S, Metzger A, Müller M, Hansel A (2013) Volatile organic compounds in the western Mediterranean basin: urban and rural winter measurements during the DAURE campaign. Atmos Chem Phys 13:4291–4306CrossRefGoogle Scholar
  45. Simpson D (1995) Hydrocarbon reactivity and ozone formation in Europe. J Atmos Chem 20:163–177CrossRefGoogle Scholar
  46. Simpson JE (1994) Sea breeze and local wind. Cambridge University Press, Cambridge, 234 ppGoogle Scholar
  47. Sokolov A, Augustin P, Dmitriev E, Delbarre H, Talbot C, Fourmentin M (2013) Simulation of local atmospheric dynamics in the coastal region of Dunkerque. Russian Meteorol Hydrol 38(2):100–105CrossRefGoogle Scholar
  48. Stein J, Richard E, Lafore JP, Pinty JP, Asencio N, Cosma S (2000) High-resolution non-hydrostatic simulations of flash-flood episodes with grid-nesting and ice-phase parametrization. Meteorol Atmos Phys 72:101–110CrossRefGoogle Scholar
  49. Stohl A, Hittenberger M, Wotawa G (1998) Validation of the Lagrangian particle dispersion model FLEXPART against large scale tracer experiments. Atmos Environ 32:4245–4264CrossRefGoogle Scholar
  50. Stull RB (1988) An introduction to boundary layer meteorology. Kluwer Academic, Dordrecht 666 ppCrossRefGoogle Scholar
  51. Talbot C, Augustin P, Leroy C, Delbarre H, Khomenko G, Willart V (2007) Impact of a sea breeze on the boundary layer dynamics and atmospheric stratification in a coastal area of the North Sea. Boundary-Layer Meteorol 125:133–154CrossRefGoogle Scholar
  52. Wang F, Chen DS, Cheng SY, Li JB, Li MJ, Ren ZH (2010) Identification of regional atmospheric PM10 transport pathways using HYSPLIT, MM5-CMAQ and synoptic pressure pattern analysis. Environ Model Softw 25(8):927–934CrossRefGoogle Scholar
  53. WHO (2005) Air quality guidelines global update 2005: particulate matter, ozone, nitrogen dioxide and sulfur dioxide. World Health Organization, Regional Office for Europe; 2006, Copenhagen.
  54. Xiang Y, Delbarre H, Sauvage S, Léonardis T, Fourmentin M, Augustin P, Locoge N (2012) Environ Pollut 162:15–28CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  • Anton Sokolov
    • 1
  • Egor Dmitriev
    • 2
  • Elena Maksimovich
    • 1
  • Hervé Delbarre
    • 1
  • Patrick Augustin
    • 1
  • Cyril Gengembre
    • 1
  • Marc Fourmentin
    • 1
  • Nadine Locoge
    • 3
  1. 1.Laboratory for Physico-Chemistry of the AtmosphereUniversity of Littoral Cote d’OpaleDunkerqueFrance
  2. 2.Institute of Numerical Mathematics of Russian Academy of ScienceMoscowRussia
  3. 3.Sciences de l’Atmosphère et Génie de l’EnvironnementEcole Nationale Supérieure des Mines de DouaiDouaiFrance

Personalised recommendations