Special Issue on Air Quality in a Changed World: Regional, Ambient, and Indoor Air Concentrations from the COVID to Post-COVID Era

Ludmilla Manera Conti1, Dirceu Luís Herdies  This email address is being protected from spambots. You need JavaScript enabled to view it.1, Débora Souza Alvim1,2, Sergio Machado Corrêa3

1 National Institute for Space Research, Cachoeira Paulista, SP 12630-000, Brazil
2 Lorena School of Engineering (EEL), University of Sao Paulo (USP), Lorena 05508-050, SP, Brazil
3 Faculty of Technology, Rio de Janeiro State University (UERJ), Resende, RJ 27537-000, Brazil

Received: November 30, 2021
Revised: April 14, 2022
Accepted: June 10, 2022

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation: ||https://doi.org/10.4209/aaqr.210364  

Cite this article:

Conti, L.M., Herdies, D.L., Alvim, D.S., Corrêa, S.M. (2022). Analysis of the Effect of the Truck Strike and COVID-19 on the concentration of NOx and O3 in the Metropolitan Region of the Vale do Paraiba, São Paulo, Brazil. Aerosol Air Qual. Res. https://doi.org/10.4209/aaqr.210364


  • During the truckers´ strike and COVID-19 the NO concentrations decreased by 58%–70%.
  • Due the lockdown imposed by the pandemic there was an increase of O3 by 24%.
  • Effects on pollutant concentrations were not affected by meteorological parameters.


The daily diurnal data pattern of nitrogen oxides (NO, NO2) and ozone (O3), temperature, relative humidity, pressure, wind direction and speed and solar radiation were studied from 2017 to 2020 within a period of 21 days in two towns in Paraiba Valley: São José dos Campos (SJC) and Guaratinguetá (GRT). In 2018, there was a truckers' strike in Brazil and in 2020 a partial lockdown was imposed in response to the coronavirus pandemic; in this study, Machine Learning techniques and a multivariate statistical analysis were conducted to compare these different periods. During both 2018 and 2020, there was a reduction in the NO and NO2 concentrations, (particularly NO), which is a primary pollutant during peak hours of vehicular traffic; this was notably the case in 2018 owing to the truckers´ strike. Through an application of the Tukey test, a comparison was made between the NO, NO2 and O3 data which showed that there was a similarity in each element of the dataset on a decreasing scale, however they continue to be statistically significant. Regarding the Principal Component Analysis (PCA), this procedure identified the first major component for both towns in the entire study period and explained around 42% of the data and the proper interconnections between the data, with a strong positive influence of O3 concentrations, temperature (T), wind speed (WS) and solar radiation (SR). In addition, when analyzing data by means of the Boruta algorithm, there was a considerable difference in the variables that influence O3 concentrations, with GRT showing NO2 and relative humidity, while SJC, NO2 and global solar radiation were the most important variables for feature selection.

Keywords: Air Pollution, COVID-19 pandemic, Strike, Machine Learning, PCA

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