Advertisement

Environmental Fluid Mechanics

, Volume 7, Issue 2, pp 95–119 | Cite as

The impact on tropospheric ozone formation on the implementation of a program for mobile emissions control: a case study in São Paulo, Brazil

  • Odón R. Sánchez-Ccoyllo
  • Leila Droprinchinski Martins
  • Rita Y. Ynoue
  • Maria de Fátima Andrade
Original Article

Abstract

The main sources of reactive hydrocarbons (RHC) and nitrogen oxides (NOx), ozone precursors, in the Metropolitan Area of São Paulo (MASP) in the southeast of Brazil are emissions from vehicles fleets. Ambient surface ozone and particulate matter concentrations are air quality problem in the MASP. This study examined the impact that implementing a control program for mobile emissions has on ozone concentrations, An episode of high surface ozone concentrations occurring in the MASP during the March 13–15, 2000 period was used as a case study that was modeled for photochemical oxidants using the California Institute of Technology/Carnegie Mellon University three-dimensional photochemical model. Different scenarios were analyzed in relationship to the implementation of the Programa Nacional de Controle de Poluição por Veículos Automotores (PROCONVE, National Program to Control Motor Vehicle Pollution). Scenario 1 assumed that all vehicles were operating within PROCONVE guidelines. Scenarios 2 and 3 considered hypothetical situations in which the PROCONVE was not implemented. Scenario 2 set the premise that vehicles were using pre-1989 technology, whereas scenario 3 allowed for technological advances. A base case scenario, in which the official emission inventory for the year 2000 was employed, was also analyzed. The CIT model results show agreement with most measurements collected during 13–15 March 2000 modeling episode. Mean normalized bias for ozone, CO, RHC and NO x are approximately 9.0, 6.0, −8.3, 13.0%, respectively. Tropospheric ozone concentrations predicted for scenario 2 were higher than those predicted for scenarios 1, 3 and base case. This study makes a significant contribution to the evaluation of air quality improvement and provides data for use in evaluating the economic costs of implementing a program of motor vehicle pollution control aimed at protecting human health.

Keywords

Air quality CIT model Emission inventory Ozone São Paulo 

Abbreviations

CETESB

Companhia de Tecnologia de Saneamento Ambiental (Environmental Sanitation Technology Company)

CIT model

Photochemical Model developed jointly by Carnegie Mellon University and the California Institute of Technology

IAG

Institute of Astronomy, Geophysics and Atmospheric Sciences

MASP

Metropolitan Area of São Paulo

NOx

Oxides of nitrogen

ppbv

Parts per billion by volume

PROCONVE

Programa Nacional de Controle de Poluição por Veículos Automotores (National Program to Control Motor Vehicle Pollution)

RAMS

Regional atmospheric modeling system

SAPRC

Statewide Air Pollution Research Center

SODAR

Sound detection and ranging

USP

University of São Paulo

VOC

Volatile organic compound

RHC

Reactive hydrocarbons

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Sillman S (1999) The relation between ozone, NOx and hydrocarbons in urban and polluted rural environments. Atmos Environ 33:1821–1845CrossRefGoogle Scholar
  2. 2.
    Held T, Ying Q, Kaduwela A, Kleeman M (2004) Modeling particulate matter in the San Joaquin Valley with a source-oriented externally mixed three-dimensional photochemical grid model. Atmos Environ 38:3689–3711CrossRefGoogle Scholar
  3. 3.
    Mellios G, Aalst VR, Samaras Z (2006) Validation of road traffic urban emission inventories by means of concentration data measured at aair quality monitoring station in Europe. Atmos Environ 40:7362–7377CrossRefGoogle Scholar
  4. 4.
    La Rovere EL (2002) Avaliação do PROCONVE programa de Controle da Poluição do Ar por Veículos Automotores. Laboratório Interdisciplinar de Meio Ambiente- COPPE/UFRJ. Universidade Federal de Rio Janeiro, Rio de Janeiro, Brasil, pp 187Google Scholar
  5. 5.
    McRae GJ, Goodin WR, Seinfeld JH (1982a) Development of second-generation mathematical model for urban air pollution- I. Model Formulation. Atmos Environ 16:679–696Google Scholar
  6. 6.
    Cotton WR, Pielke RA, Walko S, Liston RL, Tremback GE, Jiang HC, McAnelly RL, Harrington JY, Nicholls ME, Carrio GG, McFadden JP (RAMS 2001: 2003) Current status and future directions. Meteorol Atmos Phys 82:5–29Google Scholar
  7. 7.
    Pielke RA, Cotton WR, Walko RL, Tremback CJ, Lyons WA, Grasso LD, Nicholls ME, Moran MD, Wesley DA, Lee TJ, Copeland JH (1992) A Comprehensive meteorological modeling system – RAMS. Meteorol Atmos Phys 49(1–4):69–91CrossRefGoogle Scholar
  8. 8.
    Harley RA, Russell AG, McRae GJ, Cass GR, Seinfeld JH (1993) Photochemical modeling of the Southern California Air Quality Study. Envir Sci Technol 27:378–388CrossRefGoogle Scholar
  9. 9.
    Burian SJ, Streit GE, McPherson TN, Brown MJ, Turin HJ (2001) Modeling the atmospheric deposition and stormwater washoff of nitrogen compounds. Env Model S 16:467–479CrossRefGoogle Scholar
  10. 10.
    Sánchez-Ccoyllo OR, Ynoue YR, Martins DL, Andrade FM (2006) Impacts of ozone precursor limitation and meteorological variables on ozone concentration in Sao Paulo, Brazil. Atmos Environ 40:S552-S562CrossRefGoogle Scholar
  11. 11.
    Vivanco GM, Andrade FM (2006) Validation of the emission inventory in the São Paulo Metropolitan área of Brazil, base don ambient concentrations ratios of CO, NMOG, and NOx and on a photochemical model. Atmos Environ 40:1189–1198CrossRefGoogle Scholar
  12. 12.
    Ulke AG, Andrade MF (2001) Modeling urban air pollution in Sao Paulo, Brazil: sensitivity of model predicted concentrations to different turbulence parameterizations. Atmos Environ 35:1747–1763CrossRefGoogle Scholar
  13. 13.
    CETESB (2004) Relátorio de Qualidade do Ar no Estado de São Paulo-2003. Série Relatórios ISSN-0103–4103. Companhia de Tecnologia de Saneamento Ambiental, São Paulo, Brazil. http://www.cetesb.sp.gov.brGoogle Scholar
  14. 14.
    Silva Dias MAF, Machado AJ (1997) The role of local circulations in summertime convective development and nocturnal fog in São Paulo, Brazil. Boundary-Layer Met 82:135–157CrossRefGoogle Scholar
  15. 15.
    Kuebler J, Giovannoni J, Russel AG (1996) Eulerian modeling of photochemical pollutants over the Swiss plateau and control strategy analysis. Atmos Environ 30:951–966CrossRefGoogle Scholar
  16. 16.
    McRae JG, Tilden WJ, Seinfeld HJ (1982b) Global sensitivity analysis-A computiational implementation of the Fourier Amplitude Sensitivity Test (FAST). Comp Chem Eng 6:15–25CrossRefGoogle Scholar
  17. 17.
    Falls HA, McRae JG, Seinfeld HJ (1979) Sensitivity and uncertainty of reaction mechanisms for photochemical air pollution. Int J chem Kinet XI:1137–1162CrossRefGoogle Scholar
  18. 18.
    Koda M, Dogru AH, Seinfeld JH (1979) Sensitivity analysis of partial differential equations with applications to reaction and diffusiom processes. J Computat Phys 30(2):259–283CrossRefGoogle Scholar
  19. 19.
    Seefeld S, Stockwell RW (1999) First-order sensitivity analysis of models with time-depent parameters: an application to PAN and ozone. Atmos Environ 33:2941–2953CrossRefGoogle Scholar
  20. 20.
    Daescu ND, Sandu A, Carmichael RG (2003) Direct and adjoint sensitivity analysis of chemical kinetic systems with Kinetic Pre-Processor: II- Numerical validations and applications. Atmos Environ 37:5097–5114CrossRefGoogle Scholar
  21. 21.
    Kioutsioukis I, Tarantola S, Saltelli A, Gatelli D (2004) Uncertainty and global sensitivity analysis of road transport emission estimates. Atmos Environ 38:6609–6620CrossRefGoogle Scholar
  22. 22.
    Mulholland M, Seinfeld JH (1995) Inverse air pollution modeling of urban-scale carbon monoxide emissions. Atmos Environ 29:497–516CrossRefGoogle Scholar
  23. 23.
    Nguyen K, Dabdub D (2003) Development and analysis of a non-splitting solution for three dimensional air quality models. Atmos Environ 37:3741–3748CrossRefGoogle Scholar
  24. 24.
    Carter WPL (2000a) Implementation of the SAPRC-99 chemical mechanism into the models-3 framework “Report to the United States environmental protection agency”. January, 29, http://pah.cert.ucr.edu/∼carter/reactdat.htmGoogle Scholar
  25. 25.
    Carter WPL (2000b) Documentation of the SAPRC-99 chemical mechanism for VOC reactivity assessment. Final report to california air resources board contract No. 92–329, and (in part) 95–308, May 8, http://pah.cert.ucr.edu/∼carter/reactdat.htmGoogle Scholar
  26. 26.
    Carter WPL (1990) A detailed mechanism for the gas-phase atmospheric reactions of organic compounds. Atmos Environ 24A:481–518Google Scholar
  27. 27.
    Carter WPL (1994) Development of ozone reactivity scales for volatile organic compounds. J Air Waste Manage Assoc 44:881–899Google Scholar
  28. 28.
    Carter WPL, Atkinson R (1996) Development and evaluation of a detailed mechanism for the atmospheric reactions of isoprene and NOx. Int J Chem Kinet 28:497–530CrossRefGoogle Scholar
  29. 29.
    Kim JY, Ghim YS (2002) Effects of the density of meteorological observations on the diagnostic wind fields and the performance of photochemical modeling in the greater Seoul area. Atmos Environ 36:201–212CrossRefGoogle Scholar
  30. 30.
    Liston GE, Pielke RA (2001) A climate version of the regional atmospheric modeling system. Theor Appl Climatol 68:155–173CrossRefGoogle Scholar
  31. 31.
    Gesch DB, Verdin KL, Greenlee SK (1999) New land surface digital elevation model covers the Earth. EOS Trans Amer Goeophys Un 80(6):69–70CrossRefGoogle Scholar
  32. 32.
    Andrade MF, Ynoue RY, Harley R, Miguel AH (2004) Air-quality model simulating photochemical formation of pollutants: the São Paulo metropolitan area, Brazil. Int J Environ Pollut 22(4):460–475CrossRefGoogle Scholar
  33. 33.
    CETESB (2001) Relátorio de Qualidade do Ar no Estado de São Paulo-2000. Série Relatórios ISSN-0103–4103. Companhia de Tecnologia de Saneamento Ambiental, São Paulo, Brazil. http://www.cetesb.sp.gov.brGoogle Scholar
  34. 34.
    Goodin WR, McRae GJ, Seinfeld JH (1979) A comparison of interpolation methods for sparse data: application to wind and concentration fields. J Appl Meteorol 18:761–771CrossRefGoogle Scholar
  35. 35.
    Nair KN, Freitas ED, Sánchez-Ccoyllo OR, Silva Dias MAF, Silva Dias PL, Andrade MF, Massambani O (2004) Dynamics of urban boundary layer over São Paulo associated with mesoscale processes. Meteorol Atmos Phys 86(1–2):87–98CrossRefGoogle Scholar
  36. 36.
    Barnes SL (1973) Mesoscale objective map analysis using weighted time-series observations. NOAA technical memorandum ERL NSSL-62, National Oceanic and Atmospheric Administration, NormanGoogle Scholar
  37. 37.
    McRae GJ (1981) Mathematical modeling of photochemical air pollution, Ph.D. Thesis, California Institute of Technology, Pasadena, CaliforniaGoogle Scholar
  38. 38.
    McRae GJ, Russell AG, Harley RA (1992) CIT photochemical airshed model- Systems Manual, Carnegie Mellon University, Pittsburgh, Pennsylvania and California Institute of Technology, Pasadena, CaliforniaGoogle Scholar
  39. 39.
    Peterson JT (1976) Calculated actinic fluxes (290–700 nm) for air pollution photochemistry applications US environmental protection agency: reasearch triangle parck, NC, EPA-600/4–76–025 pp 63Google Scholar
  40. 40.
    Lyons WA, Tremback CJ, Pielke RA (1995) Applications of the regional atmospheric modeling system (RAMS) to provide input to photochemical grid models for the Lake Michigan ozone study (LMOS). J Appl Met 34:1762–1786CrossRefGoogle Scholar
  41. 41.
    Pielke RA, Uliaz M (1998) Use of meteorological models as input to regional and mesoscale air quality models-limitations and strengths. Atmos Environ 32:1455–1466CrossRefGoogle Scholar
  42. 42.
    Russell GA, Winner AD, Harley AR, McCue FK, Cass RG (1993) Mathematical modeling and control of the dry deposition flux of nitrogen-containing air pollutants. Environ Sci Technol 27(13):2772–2782CrossRefGoogle Scholar
  43. 43.
    Alonso DC, Martins BRHM, Romano J, Godinho R (1997) São Paulo aerosol characterization study. J Air Waste Manage Assoc 47:1297–1300Google Scholar
  44. 44.
    DETRAN (2001) http://www.detran.sp.gov.br (accessed in September 2004)Google Scholar
  45. 45.
    Martins LD, Andrade MF, Freitas ED, Pretto A, Gatti LV, Albuquerque EL, Tomaz E, Guardani ML, Martins MHRB, Junior OMA (2006) Emission factors for gas-powered vehicles traveling through road tunnels in São Paulo city, Brazil. Environ Sci Technol 40:6722–6729CrossRefGoogle Scholar
  46. 46.
    Sánchez-Ccoyllo OR, Ynoue YR, Martins DL, Astolfo, Miranda MR, Freitas DE, Borges SA, Fornaro A, Moreira A, Maria F, Andrade FM (2006) Vehicular particulate matter emissions from road tunnels in Sao Paulo city, Brazil. Transportation. Res Part D: Transport Environ (Submitted)Google Scholar
  47. 47.
    Colón M, Pleil JD, Hartlage TA, Guardani ML, Martins MH (2001) Survey of volatile organic compounds associated with automotive emissions in the urban airshed of São Paulo, Brazil. Atmos Environ 35:4017–4031CrossRefGoogle Scholar
  48. 48.
    Sexton K, Westburg HH (1983) Photochemical ozone formation from Petroleum Refinery emissions. Atmos Environ 17:467–475CrossRefGoogle Scholar
  49. 49.
    Harley AR, Sawyer FR, Milford BJ (1997) Updated photochemical modeling for California’s south cost air caisn: comparison of chemical mechanisms and motor vehicle emission inventories. Environ Sci Technol 31:2829–2839CrossRefGoogle Scholar
  50. 50.
    Baertsch-Ritter N, Prevot AH, Dommen J, Andreani-Aksoyoglu S, Keller J (2003) Model study with UAM-V in the Milan area (I) during PIPAPO: simulations with changed emissions compared to ground and airborne measurements. Atmos Environ 37:4133–4147CrossRefGoogle Scholar
  51. 51.
    Winner DA, Cass GR, Harley RA (1995) Effect of alternative boundary conditions on predicted ozone control strategy performance: A case study in the los Angeles Area. Atmos Environ 29:3451–3464CrossRefGoogle Scholar
  52. 52.
    Sanchez-Ccoyllo OR, Silva Dias PL, Andrade MF, Freitas SR (2005) Determination of O3, CO and PM10 transport in the metropolitan area of São Paulo, Brazil through synoptic scale analysis of back trajectories. Meteorol Atmos Phys 92:83–93CrossRefGoogle Scholar
  53. 53.
    Freitas RS, Longo MK, Silva Dias MAF, Silva Dias PL, Chatfield R, Prins E, Artaxo P, Grell GA, Recuero SF (2005) Monitoring the transport of biomass burning emissions in South America. Environ Fluid Mech 5:135–167CrossRefGoogle Scholar
  54. 54.
    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 (2002) Global climatological features in a simulation using the CPTEC-COLA AGCM. J Climate 15(21):2965–2988CrossRefGoogle Scholar
  55. 55.
    Mellor GL, Yamada T (1982) Development of a turbulence closure model for geophysical fluid problems. Rev Geophys Space Phys 20(4):851–875Google Scholar
  56. 56.
    Smagorinski J (1963) General circulation experiments with the primitive equations. Part I, the basic experiment. Mon Wea Rev 91:99–164Google Scholar
  57. 57.
    Harrington JY (1997) The effects of radiative and microphysical processes on simulated warm and transition season Artic stratus. Ph.D. dissertation. Atmos Sci Paper No. 637, Department of Atmospheric Science, Colorado State University, Fort Collins, CO, 80523, 1–289Google Scholar
  58. 58.
    Climanalise (2000) Boletim de Monitoramento e Análise Climática, 15(3) March 2000, http:/www.cptec.inpe.br (accessed in April 2004)Google Scholar
  59. 59.
    Sánchez-Ccoyllo OR, Andrade MF (2002) The influence of meteorological conditions on the behaviour of pollutants concentrations in São Paulo. Environ Pollut 116:257–263CrossRefGoogle Scholar
  60. 60.
    Chandrasekar A, Philbrick CR, Doddridge B, Clark R, Georgopoulos P (2003) A comparison study of RAMS simulations with aircraft, wind profiler, lidar, tethered balloon and RASS data over Philadelphia during a 1999 summer episode. Atmos Environ 37:4973–4984CrossRefGoogle Scholar
  61. 61.
    Lee RF, Irwin JS (1995) Methodology for a comparative evaluation of 2 air-quality models. Int J Environ Pollut 5(4–6):723–733Google Scholar
  62. 62.
    Seigneur C, Pun B, Pai P, Louis JF, Solomon P, Emery C, Morris R, Zaniser M, Worsnop D, Koutrakis P, White W, Tombach I (2000) Guidance for the performance evaluation of thee-dimensional air quality modeling systems for particulate matter and visibility. J Air Waste Manage Assoc 50:588–599Google Scholar
  63. 63.
    US EPA (US Environmental Protection Agency) (1995) Guideline for regulatory application of the urban airshed model. Research Triangle Park, Noth Carolina, 27711Google Scholar
  64. 64.
    US EPA (US Environmental Protection Agency) (2005) Guidance on the use of models and other analyses in attainment demonstrations for the 8-hour ozone NAAQS, EPA-454/R-05–002, http://www.epa.gov/scram001/ (accessed in October 2006)Google Scholar
  65. 65.
    Cheng LW (2000) A vertical profile of ozone concentration in the atmospheric boundary layer over central Taiwan. Meteorol Atmos Phys 75:251–258CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, Inc. 2007

Authors and Affiliations

  • Odón R. Sánchez-Ccoyllo
    • 1
  • Leila Droprinchinski Martins
    • 1
  • Rita Y. Ynoue
    • 1
  • Maria de Fátima Andrade
    • 1
  1. 1.Department of Atmospheric Sciences at the Institute of Astronomy, Geophysics and Atmospheric SciencesUniversity of São PauloSão PauloBrazil

Personalised recommendations