## 4 Conclusions  
This study successfully employs two different approaches to estimate the effect of a national strike of 2019 in Ecuador on concentrations of urban trace gases (NO\({}_{2}\), CO, O\({}_{3}\) and SO\({}_{2}\)): (i) observational inter-period analysis of air quality differences between the pre-strike period versus the periods during and post-strike in the capital city; and (ii) comparing a machine learning (ML) gradient boosting machine (GBM) modeled business-as-usual (BAU) conditions in Quito with the observed concentrations of atmospheric gaseous pollutants. Unexpectedly, the two approaches agree closely for urban NO\({}_{2}\) (ML: \(-31.5\pm\%\), inter-period: \(-32.36\pm 15.39\%\)), CO (ML: \(-15.55\pm\%\), inter-period: \(-19.85\pm 6.67\%\)) and O\({}_{3}\) (ML: \(-6.82\pm\%\), inter-period: \(-5.18\pm 7.32\%\)) concentration changes during the strike. The ML approach overestimates the overall reduction of city SO\({}_{2}\) concentrations (ML: \(-17.65\pm\%\), inter-period: \(-7.20\pm 7.48\%\)) during the national protest, which might show a limitation of this method on predicting SO\({}_{2}\) using selected meteorological parameters. We also estimate the effect of a national strike up to 12-days after the protest is over. While both approaches agree on a continuous decrease in O\({}_{3}\) pollution, suggesting the changes in precursor abundance even post-strike, they could not be more different for the other three gaseous pollutants. The observation-only inter-period approach underestimates the long-lasting effect on NO\({}_{2}\) reduction (ML: \(-12.43\pm 6.25\%\), inter-period: \(-2.79\pm 5.82\%\)) and disagrees on the protest effects on CO (ML: \(-4.29\pm 6.50\%\), inter-period: \(1.62\pm 5.83\%\)) and SO\({}_{2}\) (ML: \(-9.71\pm 32.25\%\), inter-period: \(3.29\pm 13.56\%\)) after the protest ended. We conclude that overall, the GBM model is more robust than the inter-period comparison method for predicting the true effect of an unusual event such as a protest on urban air quality. Nevertheless, some contaminants are more difficult to predict than others. This is the case of SO\({}_{2}\), for which the prediction accuracy is not different than the baseline (i.e., the inter-period method). For this latter gas, better performance could be expected by considering additional features in the ML model (e.g., PM). This unique study presents an example from frequently violent protests, involving burning toxic barricade objects to block transport circulation, from South America. We show that the emissions from those burnings have less impact on atmospheric pollution at an urban scale than the footprint of \"normal\" city traffic. It can serve as a valuable tool to help better estimate the future changes in pollution due to changes in human activities, such as pandemics, protests or strikes. Conceptualization, R.Z. and Y.R. ; methodology, R.Z. and Y.R. ; software, R.Z., M.B. and Y.R. ; validation, R.Z., Y.R. and K.A. ; formal analysis, R.Z. and Y.R. ; investigation, R.Z., Y.R., S.B.-B. and D.M. ; resources, R.Z., Y.R., S.B.-B., D.M. and V.D. ; data curation, R.Z., V.D. and Y.R. ; writing--original draft preparation, R.Z. ; writing--review and editing, R.Z., Y.R. and K.A. ; visualization, R.Z., Y.R. and M.B. ; supervision, R.Z. ; project administration, R.Z. ; funding acquisition, R.Z., S.B.-B. and D.M. All authors have read and agreed to the published version of the manuscript. This research received funding from Universidad de Las Americas, Ecuador, for the funding of AMB.RZ.21.03 project and CEDIA (Corporacion Ecuatoriana para el Desarrollo de la Investigacion y la Academia) for the funding of CEPRA-XV-2021-018 project. Not applicable. The data was acquired from the Secretariat of the environment at [[http://datos.quitoambiente.gob.ec/descarga.html](http://datos.quitoambiente.gob.ec/descarga.html)]([http://datos.quitoambiente.gob.ec/descarga.html](http://datos.quitoambiente.gob.ec/descarga.html)) (accessed on 20 October 2021).