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Analysis of PM2.5 concentrations under pollutant emission control strategies in the metropolitan area of São Paulo, Brazil

  • Taciana T. de A. AlbuquerqueEmail author
  • Jason West
  • Maria de F. Andrade
  • Rita Y. Ynoue
  • Willian L. Andreão
  • Fábio S. dos Santos
  • Felipe Marinho Maciel
  • Rizzieri Pedruzzi
  • Vitor de O. Mateus
  • Jorge A. Martins
  • Leila D. Martins
  • Erick G. S. Nascimento
  • Davidson M. Moreira
Research Article
  • 5 Downloads

Abstract

Great efforts have been made over the years to assess the effectiveness of air pollution controls in place in the metropolitan area of São Paulo (MASP), Brazil. In this work, the community multiscale air quality (CMAQ) model was used to evaluate the efficacy of emission control strategies in MASP, considering the spatial and temporal variability of fine particle concentration. Seven different emission scenarios were modeled to assess the relationship between the emission of precursors and ambient aerosol concentration, including a baseline emission inventory, and six sensitivity scenarios with emission reductions in relation to the baseline inventory: a 50% reduction in SO2 emissions; no SO2 emissions; a 50% reduction in SO2, NOx, and NH3 emissions; no sulfate (PSO4) particle emissions; no PSO4 and nitrate (PNO3) particle emissions; and no PNO3 emissions. Results show that ambient PM2.5 behavior is not linearly dependent on the emission of precursors. Variation levels in PM2.5 concentrations did not correspond to the reduction ratios applied to precursor emissions, mainly due to the contribution of organic and elemental carbon, and other secondary organic aerosol species. Reductions in SO2 emissions are less likely to be effective at reducing PM2.5 concentrations at the expected rate in many locations of the MASP. The largest reduction in ambient PM2.5 was obtained with the scenario that considered a reduction in 50% of SO2, NOx, and NH3 emissions (1 to 2 μg/m3 on average). It highlights the importance of considering the role of secondary organic aerosols and black carbon in the design of effective policies for ambient PM2.5 concentration control.

Keywords

Air quality modeling Fine particles Emission control Atmospheric chemistry CMAQ 

Notes

Acknowledgments

This research was partially funded by Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP), Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) and Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), Brazil.

References

  1. Albuquerque TTA, Andrade MF, Ynoue RY (2012) Characterization of atmospheric aerosols in the city of São Paulo, Brazil: comparisons between polluted and unpolluted periods. Environ Monit Assess 184:969–984.  https://doi.org/10.1007/s10661-011-2013-y CrossRefGoogle Scholar
  2. Albuquerque TTA, Andrade MF, Ynoue RY, Moreira DM, Andreão WL, Santos FS, Nascimento EGS (2018) WRF-SMOKE-CMAQ modeling system for air quality evaluation in São Paulo megacity with a 2008 experimental campaign data. Environ Sci Pollut Res 25:36555–36569.  https://doi.org/10.1007/s11356-018-3583-9 CrossRefGoogle Scholar
  3. Andrade MF, Miranda RM, Fornaro A, Kerr A, Oyama B, André PA, Saldiva P (2012) Vehicle emissions and PM2.5 mass concentrations in six Brazilian cities. Air Qual Atmos Health 5:79–88.  https://doi.org/10.1007/s11869-010-0104-5 CrossRefGoogle Scholar
  4. Andrade MF, Kumar P, Freitas ED, Ynoue RY, Martins J, Martins LD, Nogueira T, Perez-Martinez P, Miranda RM, Albuquerque T, Gonçalves FLT, Oyama B, Zhang Y (2017) Air quality in the megacity of São Paulo: evolution over the last 30 years and future perspectives. Atmos Environ 159:66–82.  https://doi.org/10.1016/j.atmosenv.2017.03.051 CrossRefGoogle Scholar
  5. Andreão WL, Albuquerque TTA, Kumar P (2018) Excess deaths associated with fine particulate matter in Brazilian cities. Atmos Environ 194:71–81.  https://doi.org/10.1016/j.atmosenv.2018.09.034 CrossRefGoogle Scholar
  6. Anenberg SC, Talgo K, Dolwick P, Jang C, Arunachalam S, West JJ (2011) Impacts of global, regional, and sectoral black carbon emission reductions on surface air quality and human mortality. Atmos Chem Phys 11:7253–7267.  https://doi.org/10.5194/acp-11-7253-2011 CrossRefGoogle Scholar
  7. Binkowski FS, Roselle SJ (2003) Models-3 Community Multiscale Air Quality (CMAQ) model aerosol component 1. Model description. J Geophys Res-Atmos 108:D6.  https://doi.org/10.1029/2001JD001409 CrossRefGoogle Scholar
  8. Carvalho VSB, Freitas ED, Martins LD, Martins JA, Mazzoli CR, Andrade MF (2015) Air quality status and trends over the Metropolitan Area of São Paulo, Brazil as a result of emission control policies. Environ Sci Pol 47:68–79.  https://doi.org/10.1016/j.envsci.2014.11.001 CrossRefGoogle Scholar
  9. CETESB (2009) Relatório de Qualidade do Ar no Estado de São Paulo 2008. Companhia de Tecnologia de Saneamento Ambiental, Relatórios/CETESB ISSN 0103-4103. São Paulo (in Portuguese)Google Scholar
  10. CETESB (2014) Plano de Redução de Emissão de Fontes Estacionárias – PREFE 2014. Companhia de Tecnologia de Saneamento Ambiental, ISBN 978-85-61405-80-9. São Paulo (in Portuguese)Google Scholar
  11. CETESB 2015 Relatório de Qualidade do Ar no Estado de São Paulo 2014. Companhia de Tecnologia de Saneamento Ambiental, Relatórios/CETESB ISSN 0103-4103. São Paulo (in Portuguese)Google Scholar
  12. CETESB (2018a) Relatório de Qualidade do Ar no Estado de São Paulo 2017. Companhia de Tecnologia de Saneamento Ambiental, Relatórios/CETESB ISSN 0103-4103. São Paulo (in Portuguese)Google Scholar
  13. CETESB (2018b) Emissões veiculares no estado de São Paulo 2017. Companhia de Tecnologia de Saneamento Ambiental, Relatórios/CETESB ISSN 0103-4103. São Paulo (in Portuguese)Google Scholar
  14. Chen, F., Pielke Sr. R.A., Mitchell, K., 2001. Development and application of land-surface models for mesoscale atmospheric models: problems and promises. In: Lakshmi V. Albertson J, Schaake J (eds) Land Surface Hydrology, Meteorology, and Climate: Observations and Modeling, vol. 3 of Water Science and Application, 107-135. American Geophysical Union, Washington, DC, USA American Geophysical Union, pp 107–135.  https://doi.org/10.1029/WS003p0107
  15. Dudhia J (1989) Numerical study of convection observed during the Winter Monsoon Experiment using a mesoscale two–dimensional model. J Atmos Sci 46:3077–3107.  https://doi.org/10.1175/1520-0469(1989)046<3077:NSOCOD>2.0.CO;2
  16. Fraser MP, Cass GR (1998) Detection of excess ammonia emissions from in-use vehicles and the implications for fine particle control. Environ Sci Technol 32:1053–1057.  https://doi.org/10.1021/es970382h CrossRefGoogle Scholar
  17. Gavidia-Calderón M, Vara-Vela A, Crespo NM, Andrade MF (2018) Impact of time-dependent chemical boundary conditions on tropospheric ozone simulation with WRF-Chem: An experiment over the Metropolitan Area of São Paulo. Atmos Environ 195:112–124.  https://doi.org/10.1016/j.atmosenv.2018.09.026 CrossRefGoogle Scholar
  18. Gentner DR, Jathar SH, Gordon TD, Bahreini R, Day DA, El Haddad I, Hayes PL, Pieber SM, Platt SM, Gouw JA, Goldstein AH, Harley RA, Jimenez JL, Prevot ASH, Robinson AL (2017) A review of urban secondary organic aerosol formation from gasoline and diesel motor vehicle emissions. Environ Sci Technol 51:1074–1093.  https://doi.org/10.1021/acs.est.6b04509 CrossRefGoogle Scholar
  19. Hong SY, Dudhia J, Chen SH (2004) A revised approach to ice microphysical processes for the bulk parameterization of clouds and precipitation. Mon Weather Rev 132:103–120.  https://doi.org/10.1175/1520-0493(2004)132<0103:ARATIM>2.0.CO;2
  20. Huang RJ, Zhang Y, Bozzetti C, Ho KF, Cao JJ, Han Y, Daellenbach KR, Slowik JG, Platt SM, Canonaco F, Zotter P, Wolf R, Pieber SM, Bruns EA, Crippa M, Ciarelli G, Piazzalunga A, Schwikowski M, Abbaszade G, Schnelle-Kreis J, Zimmermann R, An Z, Szidat S, Baltensperger U, El Haddad I, Prévôt ASH (2014) High secondary aerosol contribution to particulate pollution during haze events in China. Nature 514(7521):218–222.  https://doi.org/10.1038/nature13774 CrossRefGoogle Scholar
  21. Kain JS, Fritsch JM (1990) A one-dimensional Entraining/detraining plume model and its application in convective parameterization. J Atmos Sci 47:2784–2802.  https://doi.org/10.1175/1520-0469(1990)047<2784:AODEPM>2.0.CO;2
  22. Lents J, Davis N, Kikkila N, Osses M (2004) São Paulo Vehicle Activity Study. International Sustainable Systems Research Center (ISSRC), California 86pGoogle Scholar
  23. Liu X-H, Zhang Y, Xing J, Zhang Q, Wang K, Streets DJ, Jang C, Wang W-X, Hao J-M (2010) Understanding of regional air pollution over China using CMAQ, part II. Process analysis and sensitivity of ozone and particulate matter to precursor emissions. Atmos Environ 44:3719–3727.  https://doi.org/10.1016/j.atmosenv.2010.03.036 CrossRefGoogle Scholar
  24. Liu Z, Mao X, Tu J, Jaccard M (2014) A comparative assessment of economic-incentive and command-and-control instruments for air pollution and CO2 control in China’s iron and steel sector. J Environ Manag 144:135–142.  https://doi.org/10.1016/j.jenvman.2014.05.031 CrossRefGoogle Scholar
  25. Lurmann FW, Wexler AS, Pandis SN, Musarra S, Kumar N, Seinfeld JH (1997) Modelling urban and regional aerosols – II: Application to California’s South Coast Air Basin. Atmos Environ 31:2695–2715CrossRefGoogle Scholar
  26. Martins LD, Andrade MF, Freitas ED, Pretto A, Gatti LV, Albuquerque EL, Tomaz E, Guardani ML, Martins MHR, Junior OMA (2006) Emission factors for gas-powered vehicles traveling through road tunnels in São Paulo, Brazil. Environ Sci Technol 40:6722–6729.  https://doi.org/10.1021/es052441u CrossRefGoogle Scholar
  27. Martins JA, Martins LD, Freitas ED, Mazzoli CR, Hallak R, Andrade MF (2008) Aplicação de imagens de satélite no desenvolvimento de inventários de Emissão de alta resolução. Paper presented at the XV Congresso Brasileiro De Meteorologia, São Paulo, BrazilGoogle Scholar
  28. Martins JA, Mazzoli CR, Oliveira MGL, Ynoue RY, Andrade MF, Freitas ED, Martins LD (2010) Desenvolvimento de inventários de emissão de alta resolução: Intensidade de luzes noturnas e distribuição espacial de veículos. Paper presented at the XVI Congresso Brasileiro de Meteorologia, Belém, BrazilGoogle Scholar
  29. McMurry PH, Shepherd MF, Vickery JS (2004) Particulate matter science for policy makers: A NARSTO assessment. Cambridge University Press, CambridgeGoogle Scholar
  30. Miranda RM, Andrade MF, Worobiec A, Grieken RV (2002) Characterization of Aerosol Particles in São Paulo Metropolitan Area. Atmos Environ 36:345–352.  https://doi.org/10.1016/S1352-2310(01)00363-6 CrossRefGoogle Scholar
  31. Mlawer EJ, Taubman SJ, Brown PD, Iacono MJ, Clough SA (1997) Radiative transfer for inhomogeneous atmosphere: RRTM, a validated correlated-k model for the longwave. J Geophys Res 102:16,663–16,682.  https://doi.org/10.1029/97JD00237 CrossRefGoogle Scholar
  32. Nogueira T, Cordeiro DS, Muñoz RAA, Fornaro A, Miguel AH, Andrade MF (2015) Bioethanol and biodiesel as vehicular fuels in Brazil - assessment of atmospheric impacts from the long period of biofuels use. InTech 1:377–412.  https://doi.org/10.5772/60944 Google Scholar
  33. Pérez-Martínez PJ, Miranda RM, Nogueira T, Guardani ML, Fornaro A, Ynoue R, Andrade MF (2014) Emission factors of air pollutants from vehicles measured inside road tunnels in São Paulo: case study comparison. Int J Environ Sci Technol 11:2155–2168.  https://doi.org/10.1007/s13762-014-0562-7 CrossRefGoogle Scholar
  34. Pimonsree S, Vongruang P (2018) Impact of biomass burning and its control on particulate matter over a city in mainland Southeast Asia during a smog episode. Atmos Environ 195:196–209.  https://doi.org/10.1016/j.atmosenv.2018.09.053 CrossRefGoogle Scholar
  35. San Martini FM, West JJ, Foy B, Molina LT, Molina MJ, Sosa G, McRae GJ (2005) Modeling Inorganic Aerosols and Their Response to Changes in Precursor Concentration in Mexico City. J Air Waste Manage Assoc 55:803–815.  https://doi.org/10.1080/10473289.2005.10464674
  36. Sánchez-Ccoyllo OR, Ynoue RY, Martins LD, Astolfo R, Miranda RM, Freitas ED, Borges AS, Fornaro A, Freitas H, Moreira A, Andrade MF (2009) Vehicular particulate matter emissions in road tunnels in Sao Paulo, Brazil. Environ Monit Assess 149:241–249.  https://doi.org/10.1007/s10661-008-0198-5 CrossRefGoogle Scholar
  37. Seinfeld JH, Pandis NS (2006) Atmospheric Chemistry and Physics: from Air Pollution to Climate Change, vol 2. Wiley – Interscience Publication, USAGoogle Scholar
  38. Skamarock WC, Klemp JB, Dudhia J, Gil DO, Barker DM, Duda MG, Huang X, Wang W, Powers JG (2008) A description of the advanced research WRF version 3. NCAR/TN 475 + STR Tech. Note, ColoradoGoogle Scholar
  39. Tsimpidi AP, Karydis VA, Pandis SN (2007) Response of inorganic fine particulate matter to emission changes of sulfur dioxide and ammonia: The eastern United States as a case study. J Air Waste Manage Assoc 57:1489–1498.  https://doi.org/10.3155/1047-3289.57.12.1489 CrossRefGoogle Scholar
  40. West JJ, Ansari A, Pandis SN (1999) Marginal PM2.5 – Nonlinear aerosol mass response to sulfate reductions. J Air Waste Manag Assoc 49:1415–1424.  https://doi.org/10.1080/10473289.1999.10463973 CrossRefGoogle Scholar
  41. Zhang H, Hu J, Kleeman M, Ying Q (2014) Source apportionment of sulfate and nitrate particulate matter in the Eastern United States and effectiveness of emission control programs. Sci Total Environ 490:171–181.  https://doi.org/10.1016/j.scitotenv.2014.04.064 CrossRefGoogle Scholar

Copyright information

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

Authors and Affiliations

  • Taciana T. de A. Albuquerque
    • 1
    • 2
    Email author
  • Jason West
    • 3
  • Maria de F. Andrade
    • 4
  • Rita Y. Ynoue
    • 4
  • Willian L. Andreão
    • 1
  • Fábio S. dos Santos
    • 1
  • Felipe Marinho Maciel
    • 1
  • Rizzieri Pedruzzi
    • 1
  • Vitor de O. Mateus
    • 2
  • Jorge A. Martins
    • 5
  • Leila D. Martins
    • 5
  • Erick G. S. Nascimento
    • 6
  • Davidson M. Moreira
    • 2
    • 6
  1. 1.Federal University of Minas GeraisBelo HorizonteBrazil
  2. 2.Federal University of Espírito SantoVitóriaBrazil
  3. 3.University of North CarolinaChapel HillUSA
  4. 4.University of São PauloSão PauloBrazil
  5. 5.Federal Technological University of ParanáLondrinaBrazil
  6. 6.SENAI CIMATECSalvadorBrazil

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