Exploratory study for estimating atmospheric low level particle pollution based on vertical integrated optical measurements

Atmospheric Environment

Volumn 45, Issue 23, 2011, Pages 3891-3902

  Yahi, Houda ^a,b   Santer, Richard ^a   Weill, Alain ^c   Crepon, Michel ^b   Thiria, Sylvie ^b  

^a UNIVERSITÉ DU LITTORAL CÔTE D OPALE   (France)

^b UNIVERSITÉ PIERRE ET MARIE CURIE   (France)

^c SERVICE D AÉRONOMIE DU CNRS   (France)

Author keywords

Aerosol Optical Thickness (AOT); Aerosols; Atmospheric particulate matter (PM10); Unsupervised classification; Weather types

Indexed keywords

AEROSOL OPTICAL THICKNESS; ATMOSPHERIC PARTICULATE MATTER; ATMOSPHERIC PARTICULATE MATTER (PM10); EXPLORATORY STUDIES; FUNCTIONAL RELATIONSHIP; INTEGRATED PARAMETER; LOW LEVEL; METEOROLOGICAL VARIABLES; OPTICAL MEASUREMENT; PARTICLE POLLUTION; PHOTOMETER MEASUREMENTS; SATELLITE OBSERVATIONS; STATISTICAL CONFIDENCE; SURFACE PARAMETER; TWO PARAMETER; UNSUPERVISED CLASSIFICATION; WEATHER TYPES;

ATMOSPHERIC AEROSOLS; ATMOSPHERIC STRUCTURE; OPTICAL DATA PROCESSING; SELF ORGANIZING MAPS;

PARTICLES (PARTICULATE MATTER);

AEROSOL PROPERTY; ATMOSPHERIC POLLUTION; METEOROLOGY; OPTICAL METHOD; PARTICULATE MATTER; PERFORMANCE ASSESSMENT; PHOTOMETER; SATELLITE IMAGERY; SEASONALITY;

AIR POLLUTION; ARTICLE; ATMOSPHERE; EXPLORATORY RESEARCH; FRANCE; METEOROLOGY; PARTICULATE MATTER; PHOTOMETER; PRIORITY JOURNAL; SUMMER; SUN; WEATHER;

FRANCE; LILLE; NORD [NORD PAS DE CALAIS]; NORD PAS DE CALAIS;

EID: 79957779122     PISSN: 13522310     EISSN: 18732844     Source Type: Journal    
DOI: 10.1016/j.atmosenv.2010.11.047     Document Type: Article

References (22)

1. Badran F., Yacoub M., Thiria S. Self-organizing Maps and Unsupervised Classification, in Neural Networks: Methodology and Applications 2004, Springer. G. Dreyfus (Ed.).
2. Bissolli and Dittmann, 2001. The objective weather type classification of the German weather service and its possibilities of application to environmental and meteorological investigations, METTOOLS conference No 4 Stuttgart, 10, no 4, pp. 227-281 (1 p.1/4), pp. 253-260.
3. Boyouk Neda, Léon Jean-François, Delbarre Hervé, Podvin T., Deroo C. Impact of the mixing boundary layer on the relationship between PM2.5 and aerosol optical thickness. Atmos. Environ. 2010, 44(2010):271-277.
4. Chu D.A., Kaufman Y.J., et al. Global monitoringof air pollution over land from the Earth observing system-Terra moderate imaging spectroradiometer (MODIS). J. Geophys. Res. 2003, 108(D21):4661.
5. Dubovik O., Smirnov A., Holben B.N., King M.D., Kaufman Y.J., Eck T.F., Slutsker I. Accuracy assessments of aerosol optical properties retrieved from aerosol Robotic network (AERONET) sun and sky radiance measurements. J. Geophys. Res. 2000, 105(D8):9791-9806.
6. Guèye, A.K., Serge Janicot, Niang, A., Sawadogo, S., Benjamin Sultan, A., Diongue-Niang, S. Thiria. (2009). Weather regimes over Senegal during the summer monsoon season using Self-organizing maps.
7. Hewitson B.C., Crane R.G. Self-organizing maps: applications to synoptic climatology. Clim. Res. 2002, 22:13-26.
8. Holben B.N., Eck T.F., et al. AERONET-A federated instrument network and data archive for aerosol characterisation. Rem. Sens. Environ. 1998, 66:1-16.
9. Kacenelenbogen M., Léon J.-F., Chiapello I., Tanrè D. Characterization of aerosol pollution events in France using ground-based and POLDER-2 satellite data. Atmos. Chem. Phys. 2006, 6:4843-4849.
10. Kohonen T. Self-organized formation of topologically correct feature maps. Biol. Cybern. 1982, 43:59-69.
11. Kohonen T. Self-organizing Maps 2001, Springer-Verlag, Berlin, Heidelberg, New York, 501 pp. third ed.
12. Kohonen T. Self-organization and Associated Memory 2001, Springer, Berlin, Heidelberg, New York. third ed.
13. Nemmar, A., Hoet, P.H., Dinsdale, D., Vermylen, J., M.F.
14. Niang A., Gross L., Thiria S., Badran F., Moulin C. Automatic neural classification of ocean colour reflectance spectra at the top of atmosphere with introduction of expert knowledge. Rem. Sens. Environ. 2003, 86:257-271.
15. Noordijk, H., Visser, H., Correcting air pollution concentrations for meteorological conditions; an extended regression-tree approach. Adopted from the Netherlands Environmental Balance, 2002 & 2003 editions.
16. Leszek Osrodka, Marek Wojtyla, Ewa Krajny, Rafal Dunal, Krzysztof Klejnowski, 2007. Application data mining for forecasting of hight-level air. Pollution in urban-industrial area in southern Poland.
17. Patasnick H., Rupprecht E.G. Continuous PM-10 measurement using the tapered element oscillating microbalance. J. Air Waste Manage. Assoc. 1991, 41:1079-1083.
18. Pelletier B., Santer R., Vidot J. Retrieving of particulate matter from optical measurements: a semiparametric approach. J. Geophys. Res. 2007, 112.
19. Raut J.-C., Chazette P. Assessment of vertically-resolved PM10 from mobile lidar observations. Atmos. Chem. Phys. 2009, 9(21):8617-8638.
20. Schaap M., Apituley A., Timmermans R.M.A., Koelemeijer R.B.A., de Leeuw G. Exploring the relation between aerosol optical depth and PM2.5 at Cabauw, the Netherlands. Atmos. Chem. Phys. 2008, 9:909-925.
21. Vautard R. Multiple weather regimes over the north Atlantic: analysis of precursors and successors. Mon. Wea. Rev. 1990, 118:2056-2081.
22. Yiou P., Nogaj M. Extreme climatic events and weather regimes over the north Atlantic: when and where?. Geophys. Res. Lett. 2004, 31:L07202. 10.1029/2003GL019119.