Brazilian truckers’ strike and particulate matter (PM10) concentration: Temporal trend and time series models




air pollution; trend analysis; road transport; strike impact; ARIMAX models.


High particulate matter (PM) emissions from vehicular traffic impact air quality in urban areas. In 2018, a truckers’ strike interrupted some of the services in Brazil, leading to a fuel outage in several cities that significantly reduced the flow of vehicles. This study evaluated air quality during the strike in two cities (Limeira and Campinas) in Southeastern Brazil. PM10 concentration was analyzed in the periods before (BTS — 05/01/2018 to 05/22/2018), during (DTS — 05/23/2018 to 05/30/2018), and after (ATS — 05/31/2018 to 06/30/2018) the strike using the Theil-Sen method and the Autoregressive Integrated Moving Average model with Exogenous Variables (ARIMAX). A reduction in the PM daily mean concentration in both cities occurred during the strike. Considering the daily peak time of vehicular flow (6:00 p.m.), the PM10 concentration was 20% higher in the BTS period compared to the DTS period for both cities. In comparison, the ATS period showed concentrations 17% (Limeira) and 7% (Campinas) higher when compared with the DTS period. The variations were statistically significant based on the time series models, and the influences of wind speed, rainfall on the sampling day and the day before sampling, and weekends were also evaluated. It was also possible to verify the contribution of the truckers’ strike to the PM10 concentration in the two cities evaluated. In Limeira, truck traffic had a greater influence on the concentration of PM10, while in Campinas, the contribution of trucks was like that of light vehicles. Based on the variation of the PM10 concentration, the influence of changes in vehicle emission dynamics, one of the main sources of emission in the regions studied, was observed. The results indicate that restricting vehicular traffic had an immediate impact on improving air quality. Therefore, public investment in other types of transport and traffic control policies are suggested.


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Ahani, I.K.; Salari, M.; Shadman, A., 2019. Statistical Models for multi-step-ahead forecasting of fine particulate matter in urban areas. Atmospheric Pollution Research, v. 10, (3), 689-700.

Alvares, C.A.; Stape, J.L.; Sentelhas, P.C.; Gonçalves, J.L.M.; Sparovek, G., 2013. Köppen’s Climate Classification Map for Brazil. Meteorologische Zeitschrift, v. 22, (6), 711-728.

Ancelet, T.; Davy, P.K; Trompetter, W.J. 2015. Particulate matter sources and long-term trends in a small New Zealand City. Atmospheric Pollution Research, v. 6, (6), 1105-1112.

Basagaña, X.; Triguero-Mas, M.; Agis, D.; Pérez, N.; Reche, C.; Alastuey, A.; Querol, X., 2018. Effect of public transport strikes on air pollution levels in Barcelona (Spain). Science of the Total Environment, v. 610-611, 1076-1082.

BBC, 2018. Greve dos caminhoneiros: a cronologia dos 10 dias que pararam o Brasil (Accessed September, 2022) at:.

Borba, B.S.M.C.; Lucena, A.F.P.; Cunha, B.S.L.; Szklo, A.; Schaeffer, R., 2017. Diesel Imports dependence in Brazil: a demand decomposition analysis. Energy Strategy Reviews, v. 18, 63-72.

Box, G.E.P.; Jenkins, G.M.; Reinsel, G.C.; Ljung, G.M., 2015. Time Series Analysis: Forecasting and Control. 5th ed. New Jersey: John Wiley and Sons Inc.

Brazil, 2006. Departamento Nacional de Infra-Estrutura de Transportes (DNIT). Manual de Estudos de Tráfego. Rio de Janeiro (Accessed Sept., 2021) at:.

Canteras, F.B.; Oliveira, B.F.F.; Moreira, S., 2019. Topsoil pollution in highway medians in the state of São Paulo (Brazil): determination of potentially toxic elements using synchrotron radiation total reflection X-ray fluorescence. Environmental Science and Pollution Research, v. 26, (20), 20839-20852.

Carslaw, D., 2019. The Openair Manual Open-Source Tools for Analysing Air Pollution Data. (Accessed Sept, 2022) at:.

Carslaw, D.C.; Ropkins, K., 2012. Openair - an R package for air quality data analysis. Environmental Modelling and Software, v. 27-28, 52-61.

Carvalho-Oliveira, R., Pozo, R.M.K.; Lobo, D.J.A.; Lichtenfels, A.J.F.C.; Martins-Junior, H.A.; Bustilho, J.O.W.V.; Saiki, M.; Sato, I.M.; Saldiva, P.H.N., 2005. Diesel emissions significantly influence composition and mutagenicity of ambient particles: a case study in São Paulo, Brazil. Environmental Research, v. 98, (1), 1-7.

Catalano, M.; Galatioto, F.; Bell, M.; Namdeo, A.; Bergantino, A.S., 2016. Improving the prediction of air pollution peak episodes generated by urban transport networks. Environmental Science and Policy, v. 60, 69-83.

Centro de Pesquisas Meteorológicas e Climáticas Aplicadas à Agricultura (CEPAGRI), 2019. Climatologia Campinas. (Accessed Sept., 2022) at:.

Centro Integrado de Informações Agrometeorológicas (CIIAGRO), 2019. Resenha Agrometeorológica – Quadro Chuva Mensal Por Período. (Accessed Sept., 2022) at:.

Climate-Data.Org, 2022a. Clima Campinas (Brasil). (Accessed Aug. 15, 2022) at:.

Climate-Data.Org, 2022b. Clima Limeira (Brasil). (Accessed Aug. 15, 2022) at:.

CNN, 2018. Brazil Struggles after Weeklong Truckers’ Strike. (Accessed Sept., 2022) at:.

Companhia Ambiental do Estado de São Paulo (CETESB), 2020. Relatorio de qualidade do ar no estado de Sao Paulo. (Accessed Sept., 2022) at:.

Cui, M.; Chen, Y.; Tian, C.; Zhang, F.; Yan, C.; Zheng, M., 2016. Chemical composition of PM2.5 from two tunnels with different veHICULAR FLEET CHARACTERISTICs. Science of the Total Environment, v. 550, 123-132.

Dantas, G.; Siciliano, B.; Freitas, L.; Seixas, E.G.; Silva, C.M.; Arbilla, G., 2019. Why did ozone levels remain high in Rio de Janeiro during the Brazilian truck driver strike? Atmospheric Pollution Research, v. 10, (6), 2018-2029.

Debone, D.; Leirião, L.F.L.; Miraglia, S.G.K., 2020. Air quality and health impact assessment of a truckers’ strike in Sao Paulo State, Brazil: a case study. Urban Climate, v. 34, 100687.

Departamento de Estradas e Rodagem (DER), 2019. Volume diário médio das rodovias - VDM. (Accessed Sept., 2022) at:.

Ding, L.; Chan, T.W.; Ke, F.; Wang, D.K.W., 2014. Characterization of chemical composition and concentration of fine particulate matter during a transit strike in Ottawa, Canada. Atmospheric Environment, v. 89, 433-442.

Fenech, S.; Aquilina, N.J., 2020. Trends in ambient ozone, nitrogen dioxide, and particulate matter concentrations over the Maltese Islands and the corresponding health impacts. Science of the Total Environment, v. 700, 134527.

Földi, C.; Sauermann, S.; Dohrmann, R.; Mansfeldt, T., 2018. Traffic-related distribution of antimony in roadside soils. Environmental Pollution, v. 237, 704-712.

Franzin, B.T., Guizellini, F.C.; Babos, D.V.; Hojo, O.; Pastre, I.A.; Marchi, M.R.R.; Fertonani, F.L.; Oliveira, C.M.R.R., 2020. Characterization of atmospheric aerosol (PM10 and PM2.5) from a medium sized city in São Paulo State, Brazil. Journal of Environmental Sciences, v. 89, 238-251.

G1, 2018. Cronologia: greve dos caminhoneiros. (Accessed Sept., 2022) at:.

Gonçalves, P.B.; Nogarotto, D.C.; Canteras, F.B.; Pozza, S.A., 2022. The Relationship between the number of COVID-19 cases, meteorological variables, and particulate matter concentration in a medium-sized Brazilian City. Brazilian Journal of Environmental Sciences, v. 57, (2), 167-178.

Hinds, W.C., 1999. Aerosol technology: properties, behavior, and measurement of airborne particles. 2ed ed. New York: John Wiley and Sons Inc.

Huang, X.; Tang, G.; Zhang, J.; Liu, B.; Liu, C.; Zhang, J.; Cong, L., Cheng, M.; Yan, G.; Gao, W.; Wang, Y.; Wang, Y., 2021. Characteristics of PM2.5 pollution in Beijing after the improvement of air quality. Journal of Environmental Sciences, v. 100, 1-10.

Instituto Brasileiro de Geografia e Estatística (IBGE), 2019a. Canal Cidades - Campinas. (Accessed Sept., 2022) at:.

Instituto Brasileiro de Geografia e Estatística (IBGE), 2019b. Canal Cidades - Limeira. (Accessed Sept., 2022) at:.

Instituto Nacional de Pesquisas Espaciais (INPE), 2022. Portal do monitoramento de queimadas e incêndios florestais. São Paulo. (Accessed Aug. 15, 2022) at:.

Juda-Rezler, K.; Reizer, M.; Oudinet, J.P., 2011. Determination and analysis of PM10 Source apportionment during episodes of air pollution in Central Eastern European Urban Areas: the case of wintertime 2006. Atmospheric Environment, v. 45, (36), 6557-6566.

Karacasu, M.; Er, A.; Bilgi̧, S.; Barut, H.B., 2011. Variations in traffic accidents on seasonal, monthly, daily and hourly basis: Eskisehir Case. Procedia - Social and Behavioral Sciences, v. 20, 767-775.

Kopanakis, I.; Eleftheriadis, K.; Mihalopoulos, N.; Lydakis-Simantiris, N.; Katsivela, E.; Pentari, D.; Zarmpas, P.; Lazaridis, M. 2012. Physico-chemical characteristics of particulate matter in the Eastern Mediterranean. Atmospheric Research, v. 106, 93-107.

Leirião, L.F.L.; Debone, D.; Pauliquevis, T.; Rosário, N.M.E.; Miraglia, S.G.K., 2020. Environmental and public health effects of vehicle emissions in a large metropolis: case study of a truck driver strike in Sao Paulo, Brazil. Atmospheric Pollution Research, v. 11, (6), 24-31.

Leirião, L.F.L.; Miraglia, S.G.K., 2019. Environmental and health impacts due to the violation of Brazilian emissions control program standards in Sao Paulo Metropolitan Area. Transportation Research Part D: Transport and Environment, v. 70, 70-76.

Malvestio, A.C.; Fischer, T.B.; Montaño, M., 2018. The consideration of environmental and social issues in transport policy, plan and programme making in Brazil: a systems analysis. Journal of Cleaner Production, v. 179, 674-689.

Mateus, V.L., Gioda, A., 2017. A candidate framework for PM2.5 source identification in highly industrialized urban-coastal areas. Atmospheric Environment, v. 164, 147-164.

Moeeni, H.; Bonakdari, H., 2018. Impact of normalization and input on ARMAX-ANN model performance in suspended sediment load prediction. Water Resources Management, v. 32, (3), 845-863.

Munir, S.; Chen, H.; Ropkins, K., 2013. Quantifying temporal trends in ground level ozone concentration in the UK. Science of the Total Environment, v. 458-460, 217-227.

Munir, S.; Gabr, S.; Habeebullah, T.M.; Janajrah, M.A., 2016. Spatiotemporal analysis of fine particulate matter (PM2.5) in Saudi Arabia using remote sensing data. Egyptian Journal of Remote Sensing and Space Science, v. 19, (2), 195-205.

Nogarotto, D.C.; Pozza, S.A., 2020. A review of multivariate analysis: is there a relationship between airborne particulate matter and meteorological variables? Environmental Monitoring Assessment, v. 192, 573.

Nogarotto, D.C.; Souza, F.L.C.; Ribeiro, F.N.D.; Pozza, S.A., 2021. Use of trajectory regression analysis to understand high-PM10 episodes: a case study in Limeira, Brazil. Water, Air, & Soil Pollution, v. 232, 431.

Olszowski, T.; Ziembik, Z., 2018. An alternative conception of PM10 concentration changes after short-term precipitation in urban environment. Journal of Aerosol Science, v. 121, 21-30.

Pereira, L.A.G.; Lessa, S.N., 2011. O processo de planejamento e desenvolvimento do transporte rodoviário no Brasil. Caminhos de Geografia, v. 12, (40), 26-46.

Perrone, M.R.; Vecchi, R.; Romano, S.; Becagli, S.; Traversi, R.; Paladini, F., 2019. Weekly cycle assessment of PM mass concentrations and sources, and impacts on temperature and wind speed in Southern Italy. Atmospheric Research, v. 218, 129-144.

Pinto, W.P.; Reisen, V.A.; Monte, E.Z., 2018. Inhalable Particulate matter concentration forecast, in the greater Vitória Region, ES, Brazil, Using the Sarimax Model. Engenharia Sanitária e Ambiental, v. 23, (2), 307-318.

Policarpo, N.A.; Silva, C.; Lopes, T.F.A.; Araújo, R.S.; Cavalcante, F.S.A.; Pitombo, C.S.; Oliveira, M.L.M., 2018. Road vehicle emission inventory of a Brazilian Metropolitan Area and insights for other emerging economies. Transportation Research Part D: Transport and Environment, v. 58, 172-185.

Pozza, S.A.; Bruno, R.L.; Gonçalves, J.A.S.; Coury, J.R., 2006. Vehicular emission source profile for the city of São Carlos - Brazil. CHISA 2006 - 17th International Congress of Chemical and Process Engineering.

QUALAR. 2018. Qualidade do ar no estado de São Paulo. (Accessed Sept., 2022) at:.

R Core Team, 2018. R: The R Project for Statistical Computing. A Language and Environment for Statistical Computing. (Accessed Sept., 2022) at:.

Ramírez, O.; Campa, A.M.S.; Amato, F.; Catacolí, R.A.; Rojas, N.Y.; Rosa, J., 2018. Chemical composition and source apportionment of PM10 at an urban background site in a high e altitude Latin American megacity (Bogota, Colombia). Environmental Pollution, v. 233, 142-155.

Sen, P.K., 1968. Estimates of the regression coefficient based on Kendall’s Tau. Journal of the American Statistical Association, v. 63, (324), 1379-1389.

Silva, C.B.P.; Saldiva, P.H.N.; Amato-Lourenço, L.F.; Rodrigues-Silva, F.; Miraglia, S.G.K., 2012. Evaluation of the air quality benefits of the subway system in São Paulo, Brazil. Journal of Environmental Management, v. 101, 191-196.

Singh, G.K.; Choudhary, V.; Gupta, T.; Paul, D., 2020. Investigation of size distribution and mass characteristics of ambient aerosols and their combustion sources during post-monsoon in northern India. Atmospheric Pollution Research, v. 11, (1), 170-178.

Theil, H., 1950. A rank-invariant method of linear and polynomial regression analysis. Proceedings of the Royal Netherlands Academy of Sciences, v. 53, (Part I), 386-392, (Part II), 521-525, (Part III), 1397.

Winther, M.; Slentø, E., 2010. Heavy metal emissions for Danich road transport. (Accessed Sept., 2022) at:.

Wongsathan, R.; Chankham, S., 2016. Improvement on PM-10 forecast by using hybrid ARIMAX and neural networks model for the summer season in Chiang Mai. Procedia Computer Science, v. 86, 277-280.

Zhao, N.; Liu, Y.; Vanos, J.K.; Cao, G., 2018. Day-of-week and seasonal patterns of PM2.5 concentrations over the United States: time-series analyses using the prophet procedure. Atmospheric Environment, v. 192, 116-127.

Zheng, S.; Zhang, X.; Sun, W.; Wang, J., 2019. The Effect of a new subway line on local air quality: a case study in Changsha. Transportation Research Part D: Transport and Environment, v. 68, 26-38.




How to Cite

Nogarotto, D. C., Canteras, F. B., & Pozza, S. A. (2022). Brazilian truckers’ strike and particulate matter (PM10) concentration: Temporal trend and time series models. Revista Brasileira De Ciências Ambientais (RBCIAMB), 57(3), 477–490.