Meteorology and Atmospheric Physics

, Volume 92, Issue 1–2, pp 83–93 | Cite as

Determination of O3-, CO- and PM10-transport in the metropolitan area of São Paulo, Brazil through synoptic-scale analysis of back trajectories

  • O. R. Sánchez-Ccoyllo
  • P. L. Silva Dias
  • M. de Fátima Andrade
  • S. R. Freitas


This study is aimed to qualitatively analyze the impact of remote sources on air pollution in the Metropolitan Area of São Paulo (MASP). Air-mass back trajectories from June to August of 1999 were calculated using a three-dimensional kinematic trajectory model and grouped into trajectory clusters. Correlations of individual trajectory clusters with O3, CO and PM10 concentrations were determined. In this model, trajectories were obtained using the means of the three wind velocity components (U, V and W). The three-dimensional wind field was derived from the Regional Atmospheric Modeling System, and downscaling was employed. Coarse and fine nested grids (64-km and 16-km horizontal resolution, respectively) were used. Every 12 h (at 00 and 12 UTC), a back-trajectory ensemble, using the 64-km grid, was calculated for five defined endpoints at intervals of 0.5° N, S, E and W of the MASP (λ = 23° 33′S, ϕ = 46° 45′W), that last endpoint being centered in the MASP. To analyze cluster trajectories, the five trajectory ensembles from each day were allocated into one of four clusters (northeast, southeast, southwest or northwest quadrant) based on the origin of the trajectory over 4 days. Days on which all five trajectories originated from the same quadrant were classified as “core” days. Core day concentrations of CO, O3 and PM10 during the study period were evaluated. The results show that, during the study period, air-mass back trajectories in the MASP originated from all four quadrants: northeast (32%), southeast (12%), southwest (19%) and northwest (37%). Our analysis of back-trajectory clusters in the MASP suggests a transport to ambient air of O3 precursors and O3 from the northeast region, which is associated with agricultural activities involving biomass burning.


PM10 Concentration Back Trajectory Nest Grid Regional Atmospheric Modeling System Regional Atmospheric Modeling 
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  1. Alonso, CD, Martins, MHRB, Romano, J, Godinho, R 1997São Paulo Aerosol Characterization Study.J Air Waste Manage Assoc4712971300Google Scholar
  2. Andrade, F, Orsini, C, Maenhaut, W 1994Relation between aerosol sources and meteorological parameters for inhalable atmospheric in São Paulo city, Brazil.Atmos Environ2823072315CrossRefGoogle Scholar
  3. Brankov, E, Rao, T, Porter, PS 1998A trajectory-cluster-correlation methodology for examining the long-range transport of air pollutants.Atmos Environ3215251534CrossRefGoogle Scholar
  4. Camargo, R, Silva Dias, PL 2000The mesoscale adjustment in Paranaguábay: Case study of the period 10 to 25 August 1993.Rev Bras Meteorol (in Portuguese)15113Google Scholar
  5. Cape, JN, Methven, J, Hudson, LE 2000The use of trajectory cluster analysis to interpret trace gas measurements at Mace Head, Ireland.Atmos Environ3436513663CrossRefGoogle Scholar
  6. Castanho, ADA, Artaxo, P 2001Wintertime and summertime São Paulo aerosol source apportionment study.Atmos Environ3548894902CrossRefGoogle Scholar
  7. Cavalcanti, IFA, Marengo, JA, Satyamurty, P, Nobre, CA, Trosnikov, I, Bonatti, JP, Manzi, AO, Tarasova, T, Pezzi, LP, D′Almeida, C, Sampaio, G, Castro, CC, Sanches, MB, Camargo, L 2002Global climatological features in a simulation using the CPTEC-COLA AGCM.J Climate1529652988CrossRefGoogle Scholar
  8. CETESB (2000) Relatório de qualidade do ar no Estado de São Paulo. São Paulo 1999Google Scholar
  9. CETESB (2004) Relatório de qualidade do ar no Estado de São Paulo. São Paulo 2003Google Scholar
  10. Clark, TL, Farley, RD 1984Severe downslope windstorm calculations in two and three spatial dimensions using anelastic interactive grid nesting: A possible mechanism for gustiness.J Atmos Sci41329350Google Scholar
  11. Clark, TL, Hall, WD 1991Multi-domain simulations of the time dependent Navier-Stokes equations: Benchmark error analysis of some nesting procedures.J Comp Phys92456481Google Scholar
  12. Cotton, WR, Pielke, RA, Walko, RL, Liston, GE, Tremback, C, Jiang, H, McAnelly, RL, Harrington, JY, Nicholls, ME, Carrio, GG, McFadden, JP 2003RAMS 2001: Current status and future directions.Meteorol Atmos Phys82529CrossRefGoogle Scholar
  13. Freitas SR, Longo KM, Silva Dias MAF, Artaxo P (1996) Numerical modeling of air mass trajectories from the biomass burning areas of the Amazon basin. Ann Acad Bras Sci 68 (Suplemento 1) (in Portuguese)Google Scholar
  14. Freitas, SR, Silva Dias, MAF, Dias, PLS, Longo, KM, Artaxo, P, Andreae, MO, Fischer, HS 2000A convective kinematic trajectory calculation for low-resolution atmospheric models.J Geophys Res105375386CrossRefGoogle Scholar
  15. Gesch, DB, Verdin, KL, Greenlee, SK 1999New land surface digital elevation model covers the Earth.EOS Transactions AGU806970Google Scholar
  16. Harrington JY (1997) The effects of radiative and microphysical processes on simulated warm and transition season Arctic stratus. Ph.D. Diss., Atmospheric Science Paper No. 637, Department of Atmospheric Science, Colorado Sate University, Fort Collins, CO 80523, 289 ppGoogle Scholar
  17. Herrmann, P, Hanel, G 1997Wintertime optical properties of atmospheric particles and weather.Atmos Environ3140534062CrossRefGoogle Scholar
  18. Innocentini, V 1999A successive substitution methods for the evaluation of trajectories approximating the parcel path by a linear function of space and time.Mon Wea Rev12716391650Google Scholar
  19. Kuo, HL 1974Further studies of the parameterizations of the influence of cumulus convective on large-scale flow.J Atmos Sci3112321240CrossRefGoogle Scholar
  20. Landulfo, E, Papayannis, A, Artaxo, P, Castanho, ADA, Freitas, AZ, Sousa, RF, Viera Junior, ND, Jorge, MPMP, Sánchez-Ccoyllo, OR, Moreira, DS 2003Synergetic measurements of aerosols over São Paulo, Brazil using LIDAR, sunphotometer and satellite data during the dry season.Atmos Chem Phys315231539Google Scholar
  21. Liston, GE, Pielke, RA 2001A climate version of the regional atmospheric modeling system.Theor Appl Climatol68155173CrossRefGoogle Scholar
  22. Louis, JFA 1979A parametric model of vertical eddy fluxes in the atmosphere.Bound Layer Meteor17187202Google Scholar
  23. Lupu, A, Maenhaut, W 2002Application and comparison of two statistical trajectory techniques for identification of source regions of atmospheric aerosol species.Atmos Environ3656075618CrossRefGoogle Scholar
  24. Man, CK, Shih, MY 2001Identification of sources of PM10 aerosols in Hong Kong by wind trajectory analysis.J Aerosol Sci3212131223Google Scholar
  25. Mellor, GL, Yamada, T 1982Development of a turbulence closure model for geophysical fluid problems.Rev Geophys Space Phys20851875Google Scholar
  26. Miranda, RM, Andrade, MF, Worobiec, A, Grieken, RV 2002Characterisation of aerosol particles in the São Paulo metropolitan area.Atmos Environ32345352Google Scholar
  27. Montero, L, Vasconcellos, PC, Souza, SR, Pires, MAF, Sánchez-Ccoyllo, OR, Andrade, MF, Carvalho, LRF 2001Measurements of atmospheric carboxylic acids and carbonyl compounds in São Paulo City, Brazil.Environ Sci Technol3530713081CrossRefGoogle Scholar
  28. Pettersen, S 1940Weather analysis and forecasting.McGraw-HillNew York221223Google Scholar
  29. Pielke, RA, Cotton, WR, Walko, RL, Tremback, CJ, Lyons, WA, Grasso, LD, Nicholls, ME, Moran, MD, Wesley, DA, Lee, TJ, Copeland, JH 1992A comprehensive meteorological modeling system – RAMS.Meteorol Atmos Phys496991Google Scholar
  30. Poissant, L, Bottenheim, WJ, Roussel, P 1996Multivariate analysis of a 1992 SONTOS (The Southern Ontario Oxidant Study) data subset, in Canada.Atmos Environ3021332144CrossRefGoogle Scholar
  31. Sánchez-Ccoyllo, OR, Andrade, MF 2002The influence of meteorological conditions on the behaviour of pollutants concentrations in São Paulo.Environ Pollut116257263CrossRefGoogle Scholar
  32. Saldiva, PHN, Pope, CA, Schwartz, J, Dockery, DW, Lichtenfels, AJ, Salge, JM, Barone, I, Bohm, GM 1995Air pollution and mortality in elderly people: a time-series study in São Paulo, Brazil.Arch Environ Health50159163Google Scholar
  33. Salvador, P, Artíñano, B, Alonso, DG, Querol, X, Alastuy, AS 2004Identification and characterisation of sources of PM10 in Madrid (Spain) by statistical methods.Atmos Environ38435447CrossRefGoogle Scholar
  34. Seibert, PC 1993Convergence and accuracy of numerical methods for trajectory calculations.J Appl Meteor32558566CrossRefGoogle Scholar
  35. Silva Dias, MAF, Machado, AJ 1997The role of local circulations in summertime convective development and nocturnal fog in São Paulo, Brazil.Bound Layer Meteorol82135157CrossRefGoogle Scholar
  36. Smagorinsky, J 1963General circulation experiments with the primitive equations. Part I: the basic experiment.Mon Wea Rev9199164Google Scholar
  37. Stauffer, DR, Seaman, NL 1994Multiscale four-dimensional data assimilation.J Appl Meteor33416434CrossRefGoogle Scholar
  38. Stohl, A 1996Trajectory statistics – a new method to establish source-receptor relationships of air pollutants and its application to the transport of particulate sulfate in Europe.Atmos Environ30579587CrossRefGoogle Scholar
  39. Stohl, A 1998Computation, accuracy and applications of trajectories. A review and bibliography.Atmos Environ32947966CrossRefGoogle Scholar
  40. Tsuang, B 2003Quantification on the source/receptor relationship of primary pollutants and secondary aerosols by a Gaussian plume trajectory model: Part I: theory.Atmos Environ3739813991Google Scholar
  41. Tremback CJ (1990) Numerical simulation of a mesoscale convective complex: model development and numerical results. Ph.D. Diss., Atmospheric Science Paper No. 465, Colorado State University, Dept. of Atmospheric Science, Fort Collins, CO 80523Google Scholar
  42. Vasconcellos, PC, Zacarias, D, Pires, MAF, Pool, CS, Carvalho, LRF 2003Measurements of polycyclic aromatic hydrocarbons in airborne particles from the metropolitan area of São Paulo City, Brazil.Atmos Environ3730093018CrossRefGoogle Scholar
  43. Walko, RL, Tremback, CJ, Pielke, RA, Cotton, WR 1995An interactive nesting algorithm for stretched grids and variable nesting ratios.J Appl Meteor34994999CrossRefGoogle Scholar
  44. Walko, RL, Cotton, WR, Meyers, MP, Harrington, JY 1995New RAMS cloud microphysics parametrization. Part I: the single-moment scheme.Atmos Res382962Google Scholar
  45. Woodruff, SD, Diaz, HF, Elms, JD, Worley, SJ 1998COADS release-2 data and metadata enhancements for improvements of marine surface flux fields.Phys Chem Earth23517526Google Scholar

Copyright information

© Springer-Verlag/Wien 2005

Authors and Affiliations

  • O. R. Sánchez-Ccoyllo
    • 1
  • P. L. Silva Dias
    • 1
  • M. de Fátima Andrade
    • 1
  • S. R. Freitas
    • 1
  1. 1.Department of Atmospheric SciencesInstitute of Astronomy, Geophysics and Atmospheric Sciences, University of São PauloSão PauloBrazil

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