Abstract
Previous studies suggest that Programs to Control Vehicular Emission (PCVE) restrictions have succeeded in improving air quality. However, evidence about PCVE’s long-term effects in developing countries in the Southern Hemisphere is still lacking. We analyzed the influence of PCVE restrictions on PM2.5 changes under vehicular fleet growth scenarios in Brazil. Our results show an increase in PM2.5 concentrations (up to 14%) aligned with an intensive increase (more than 70%) in the vehicular fleet between 2001 and 2010. During this period, we detected a similar pattern in more than 50% of urban spots in the South-East and other large urban centers in the South and Mid-West regions. Between 2011 and 2020, the stabilization or reduction of PM2.5 is associated with a fleet growth smoothing combined with the continuous restrictions of PCVE. This work highlights the importance of planning and limiting fleet growth beyond vehicular technological improvement and emission factor restrictions.
Graphical abstract
Trends and drivers of PM2.5 surface concentrations in Brazil.
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Data availability
We used PM2.5 surface concentrations data provided by Van Donkelaar et al. (2021) [https://sites.wustl.edu/acag/datasets/surface-pm2-5/]; emission rates data provided by Emission Database for Global Atmospheric Research (EDGAR) (EDGAR 2023) [https://edgar.jrc.ec.europa.eu/index.php/dataset_ap61]; meteorology data provided by Funk et al. (2015) [https://www.nature.com/articles/sdata201566] and McNally (2018) [https://disc.gsfc.nasa.gov/datasets/FLDAS_NOAH01_C_GL_M_001/summary]; vehicle fleet data provided by Brazilian National Secretary of Transit (SENATRAN 2023) [https://www.gov.br/transportes/pt-br/assuntos/transito/conteudo-Senatran/estatisticas-frota-de-veiculos-senatran]; and emission factors implemented by Brazilian PCVE (Transport Policy 2023) [https://www.transportpolicy.net/region/south-america/brazil/]. The geometries of Brazilian cities are provided by GADM [https://gadm.org/data.html].
Code availability
Available by request.
References
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
Andreão WL, Albuquerque TTA (2021) Avoidable mortality by implementing more restrictive fine particles standards in Brazil: an estimation using satellite surface data. Environ Res 192:110288. https://doi.org/10.1016/j.envres.2020.110288
Anenberg S, Miller J, Henze D, Minjares R (2019) A global snapshot of the air pollution-related health impacts of transportation sector emissions in 2010 and 2015. International council of clean transportation (ICCT). https://theicct.org/wp-content/uploads/2021/06/Global_health_impacts_transport_emissions_2010-2015_20190226.pdf
Arregocés HA, Rojano R, Restrepo G (2023) Health risk assessment for particulate matter: application of AirQ+ model in the northern Caribbean region of Colombia. Air Qual Atmos Health 16:897–912. https://doi.org/10.1007/s11869-023-01304-5
Burnett R, Chen H, Szyszkowicz M, Fann N, Hubbell B, Pope CA, Apte JS, Brauer M, Cohen A, Weichenthal S, Coggins J, Di Q, Brunekreef B, Frostad J, Lim SS, Kan H, Walker KD, Thurston GD, Hayes RB, Lim CC, Turner MC, Jerrett M, Krewski D, Gapstur SM, Diver WR, Ostro B, Goldberg D, Crouse DL, Martin RV, Peters P, Pinault L, Tjepkema M, Van Donkelaar A, Villeneuve PJ, Miller AB, Yin P, Zhou M, Wang L, Janssen NAH, Marra M, Atkinson RW, Tsang H, Thach TQ, Cannon JB, Allen RT, Hart JE, Laden F, Cesaroni G, Forastiere F, Weinmayr G, Jaensch A, Nagel G, Concin H, Spadaro JV (2018) Global estimates of mortality associated with longterm exposure to outdoor fine particulate matter. Proc Natl Acad Sci USA 115:9592–9597. https://doi.org/10.1073/PNAS.1803222115
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 Policy 47:68–79. https://doi.org/10.1016/j.envsci.2014.11.001
Dallmann T (2020) Brazil PROCONVE L-7 and L-8 emission standards for light-duty vehicles. International Council on Clean Transportation. https://theicct.org/publication/brazil-proconve-l-7-and-l-8-emission-standards-for-light-duty-vehicles/
Dallmann T, Façanha C (2017) International comparison of Brazilian regulatory standards for light-duty vehicle emissions. International Council on Clean Transportation. https://theicct.org/sites/default/files/publications/Brazil-LDF-Regs_White-Paper_ICCT_14042017_vF.pdf
EDGAR – Emissions database for global atmospheric research (2023) Global Air Pollutant Emissions v6.1, Annual gridmaps. https://edgar.jrc.ec.europa.eu/index.php/dataset_ap61. Accessed 27 Sep 2023.
Funk C, Peterson P, Landsfeld M, Pedreros D, Verdin J, Shukla S, Husak G, Rowland J, Harrison L, Hoell A, Michaelsen J (2015) The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes. Sci Data 2:1–21. https://doi.org/10.1038/sdata.2015.66
Gajbhiye MD, Lakshmanan S, Kumar N, Bhattacharya S, Nishad S (2023) Effectiveness of India’s Bharat Stage mitigation measures in reducing vehicular emissions. Transp Res Part D Transp Environ 115:103603. https://doi.org/10.1016/J.TRD.2022.103603
Hasheminassab S, Daher N, Ostro BD, Sioutas C (2014) Long-term source apportionment of ambient fine particulate matter (PM 2.5) in the Los Angeles Basin: a focus on emissions reduction from vehicular sources. Environ Pollut 193:54–64. https://doi.org/10.1016/j.envpol.2014.06.012
Hoinaski L, Vasques TV, Ribeiro CB, Meotti B (2022) Multispecies and high-spatiotemporal-resolution database of vehicular emissions in Brazil. Earth Syst Sci Data 14:2939–2949. https://doi.org/10.5194/essd-14-2939-2022
Huneeus N, Van der Gon HD, Castesana P, Menares C, Granier C, Granier L, Alonso M, Andrade MF, Dawidowski L, Gallardo L, Gomez D, Klimont Z, Janssens-Maenhout G, Osses M, Puliafito SE, Rojas N, Sánchez-Ccoyllo O, Tolvett S, Ynoue RY (2020) Evaluation of anthropogenic air pollutant emission inventories for South America at national and city scale. Atmos Environ 235:117606. https://doi.org/10.1016/j.atmosenv.2020.117606
Hussain MM, Mahmud I (2019) pyMannKendall: a python package for non parametric Mann Kendall family of trend tests. J Open Source Softw 4:1556. https://doi.org/10.21105/JOSS.01556
IBGE - Instituto Brasileiro de Geografia e Estatística (2015) Áreas urbanizadas do Brasil. https://biblioteca.ibge.gov.br/index.php/bibliotecacatalogo?id=2100639&view=detalhes. Accessed 12 Aug 2023
IBGE – Instituto Brasileiro de Geografia e Estatística (2023) Classificação Rural e Urbana. https://www.ibge.gov.br/geociencias/organizacao-do-territorio/tipologias-do-territorio/15790-classificacao-rural-e-urbana.html?edicao=37670&t=acesso-ao-produto. Accessed 20 Sep 2023
Kendall MG (1975) Rank Correlation Methods, 4 edn. Charles Griffin, London
Lyu M, Bao X, Zhu R, Matthews R (2020) State-of-the-art outlook for light-duty vehicle emission control standards and technologies in China. Clean Technol Environ Policy 22:757–771. https://doi.org/10.1007/S10098-020-01834-X
Mann HB (1945) Nonparametric tests against trend. Econometrica. https://doi.org/10.2307/1907187
McNally A. - NASA/GSFC/HSL (2018) FLDAS noah land surface model L4 global monthly 0.1 × 0.1 degree (MERRA-2 and CHIRPS). Goddard earth sciences data and information services center (GES DISC) (Accessed 20 Sep 2023). 10.5067/5NHC22T9375G
Moran PAP (1950) Notes on continuous stochastic phenomena. Biometrika 37:17. https://doi.org/10.2307/2332142
Pacheco MT, Parmigiani MMM, Andrade MF, Morawska L, Kumar P (2017) A review of emissions and concentrations of particulate matter in the three major metropolitan areas of Brazil. J Transp Heal 4:53–72. https://doi.org/10.1016/j.jth.2017.01.008
Transport Policy (2023) Brazil Region. https://www.transportpolicy.net/region/south-america/brazil/. Accessed 20 Sep 2023
Rey SJ, Anselin L (2010) PySAL: a Python Library of Spatial Analytical Methods. Handb. Appl. Spat. Anal 1:175–193. https://doi.org/10.1007/978-3-642-03647-7_11
Ribeiro FND, Umezaki AS, Chiquetto JB, Santos I, Machado PG, Miranda RM, Almeida PS, Simões AF, Mouette D, Leichsenring AR, Ueno HM (2021) Impact of different transportation planning scenarios on air pollutants, greenhouse gases and heat emission abatement. Sci Total Environ 781:146708. https://doi.org/10.1016/j.scitotenv.2021.146708
Ribeiro CB, Rodella FHC, Hoinaski L (2022) Regulating light-duty vehicle emissions: an overview of US, EU, China and Brazil programs and its effect on air quality. Clean Technol Environ Policy 24:851–862. https://doi.org/10.1007/S10098-021-02238-1
Rocha R, Atun R, Massuda A, Rache B, Spinola P, Nunes L, Lago M, Castro MC (2021) Effect of socioeconomic inequalities and vulnerabilities on health-system preparedness and response to COVID-19 in Brazil: a comprehensive analysis. Lancet Glob Heal 9:782–792. https://doi.org/10.1016/S2214-109X(21)00081-4
Sen PK (1968) Estimates of the regression coefficient based on Kendall’s Tau. J Am Stat Assoc. https://doi.org/10.1080/01621459.1968.10480934
SENATRAN (2023) Estatísticas - Frota de Veículos—Ministério dos Transportes. https://www.gov.br/transportes/pt-br/assuntos/transito/conteudo-Senatran/estatisticas-frota-de-veiculos-senatran. Accessed 20 Sep 2023
Sun Z, Yang L, Bai X, Du W, Shen G, Fei J, Wang Y, Chen A, Chen Y, Zhao M (2019) Maternal ambient air pollution exposure with spatial-temporal variations and preterm birth risk assessment during 2013–2017 in Zhejiang Province. China Environ Int 133:105242. https://doi.org/10.1016/J.ENVINT.2019.105242
Theil H (1950) A rank-invariant method of linear and polynomial regression analysis, Part III. Proc. R. Netherlands Acad. Sci. 12:173
Tong R, Liu J, Wang W, Fang Y (2020) Health effects of PM2.5 emissions from on-road vehicles during weekdays and weekends in Beijing, China. Atmos Environ 223:117258. https://doi.org/10.1016/j.atmosenv.2019.117258
Van Donkelaar A, Hammer MS, Bindle L, Brauer M, Brook JR, Garay MJ, Hsu NC, Kalashnikova OV, Kahn RA, Lee C, Levy RC, Lyapustin A, Sayer AM, Martin RV (2021) Monthly global estimates of fine particulate matter and their uncertainty. Environ Sci Technol 55:15287–15300. https://doi.org/10.1021/ACS.EST.1C05309
Ventura LMB, Pinto FO, Gioda A, D’Agosto MA (2020) Inspection and maintenance programs for in-service vehicles: an important air pollution control tool. Sustain Cities Soc 53:101956. https://doi.org/10.1016/j.scs.2019.101956
Vieira EVR, do Rosario NE, Yamasoe MA, Morais FG, Martinez PJP, Landulfo E, Maura de Miranda R (2023) Chemical characterization and optical properties of the aerosol in São Paulo, Brazil. Atmosphere 14(9):1460. https://doi.org/10.3390/atmos14091460
Xu Q, Li X, Wang S, Wang C, Huang F, Gao Q, Wu L, Tao L, Guo J, Wang W, Guo X (2016) Fine particulate air pollution and hospital emergency room visits for respiratory disease in urban areas in Beijing. China Plos One 11:0153099. https://doi.org/10.1371/JOURNAL.PONE.0153099
Wang YS, Chang LC, Chang FJ (2021) Explore regional PM2.5 features and compositions causing health effects in Taiwan. Environ Manage 67:176–191. https://doi.org/10.1007/s00267-020-01391-5
WHO - World Health Organization (2023) Air pollution. https://www.who.int/health-topics/air-pollution#tab=tab_2. Accessed 24 Mar 2023
Will R, Hirota M, Chaffe PLB, Santos ON, Hoinaski L (2022) Socioeconomic development role in hospitalization related to air pollution and meteorology: a study case in southern Brazil. Sci Total Environ 826:154063. https://doi.org/10.1016/J.SCITOTENV.2022.154063
Wu X, Wu Y, Zhang S, Liu H, Fu L, Hao J (2016) Assessment of vehicle emission programs in China during 1998–2013: achievement, challenges and implications. Environ Pollut 214:556–567. https://doi.org/10.1016/j.envpol.2016.04.042
Yang X, Jiang L, Zhao W, Xiong Q, Zhao W, Yan X (2018) Comparison of ground-based PM2.5 and PM10 Concentrations in China, India, and the US. Int J Environ Res Public Heal 15:1382. https://doi.org/10.3390/IJERPH15071382
Yang S, Fang D, Chen B (2019) Human health impact and economic effect for PM2.5 exposure in typical cities. Appl Energy 249:316–325. https://doi.org/10.1016/j.apenergy.2019.04.173
Zhang Q, Zheng Y, Tong D, Shao M, Wang S, Zhang Y, Xu X, Wang J, He H, Liu W, Ding Y, Lei Y, Li J, Wang Z, Zhang X, Wang Y, Cheng J, Liu Y, Shi Q, Yan L, Geng G, Hong C, Li M, Liu F, Zheng B, Cao J, Ding A, Gao J, Fu Q, Huo J, Liu B, Liu Z, Yang F, He K, Hao J (2019) Drivers of improved PM2.5 air quality in China from 2013 to 2017. Proc Natl Acad Sci USA 116:24463–24469. https://doi.org/10.1073/PNAS.1907956116
Funding
This work was supported by Fundação de Amparo à Pesquisa e Inovação de Santa Catarina—FAPESC (Project 2018TR499) and Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brasil (CAPES) (Finance Code 001).
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CBR and LH contributed to the study conceptualization. Data collection and analysis were performed by CBR. The first draft and revised manuscript were written by CBR and LH.
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Ribeiro, C.B., Hoinaski, L. PM2.5 decadal changes in Brazil: influence of vehicular fleet growth and policy to control vehicular emissions. Clean Techn Environ Policy (2024). https://doi.org/10.1007/s10098-024-02805-2
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DOI: https://doi.org/10.1007/s10098-024-02805-2