## 3 Results and Discussion
### Assessment of the Methods  
While both methods show an overall reduction in the concentrations of most gaseous pollutants during the social strike in the Ecuadorian capital, some statistics vary. During the strike, both methods show a significant reduction in the concentrations of NO\({}_{2}\) (inter-period: \(-32.36\)\(\pm\) 15.38% vs. ML model: \(-31.53\)\(\pm\) 17.22%) and CO (inter-period: \(-19.85\)\(\pm\) 6.67 vs. ML model: \(-15.55\)\(\pm\) 6.64%). In both cases, the ML method is more conservative. Again, the two methods agree a smaller reduction is observed for O\({}_{3}\) (inter-period: \(-5.18\)\(\pm\) 7.32% vs. ML model: \(-6.82\)\(\pm\) 15.11%). These agreements suggest that both approaches are able to estimate equally well the impact of the strike on urban gaseous pollution. This might be due to the fact that the comparison of observations two weeks before the unusual event is within a short enough time to be as good as an ML modeling \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{1}{c}{**ML Estimated**} & \multicolumn{2}{c}{**NO\({}_{2}\)**} & \multicolumn{2}{c}{**CO**} & \multicolumn{2}{c}{**O\({}_{3}\)**} & \multicolumn{2}{c}{**SO\({}_{2}\)**} \\ \multicolumn{1}{c}{**Change**} & **During** & **After** & **During** & **After** & **During** & **After** & **During** & **After** \\ \hline S1-Carapungo & \(-32.55\) & \(-6.73\) & \(-10.50\) & \(0.67\) & \(-17.07\) & \(-30.97\) & \(-24.19\) & \(4.92\) \\ S2-Cotocollao & ND & ND & \(-24.60\) & \(-0.62\) & \(-14.69\) & \(-32.29\) & \(28.44\) & \(37.77\) \\ S3-Belisario & \(-41.39\) & \(-12.63\) & \(-15.13\) & \(-6.23\) & \(12.43\) & \(-9.39\) & \(-35.99\) & \(-15.13\) \\ S4-Centro & \(-39.83\) & \(-12.08\) & \(-15.57\) & \(2.96\) & \(-1.21\) & \(-12.19\) & \(-33.71\) & \(-35.28\) \\ S5-Camal & \(-40.64\) & \(-13.88\) & \(-18.45\) & \(-14.72\) & \(5.14\) & \(-25.16\) & \(-21.91\) & \(-21.45\) \\ S6-Guamani & \(3.01\) & \(-5.95\) & \(-431\) & \(-13.36\) & \(-31.69\) & \(-40.75\) & \(112.33\) & \(17.76\) \\ S7-Chillos & \(-37.76\) & \(-23.31\) & \(-20.30\) & \(-10.73\) & \(-0.62\) & \(0.32\) & \(-47.40\) & \(-56.57\) \\ Overall Mean & \(-31.53\) & \(-12.43\) & \(-15.55\) & \(-2.29\) & \(-6.82\) & \(-21.49\) & \(-17.65\) & \(-9.71\) \\ \cline{2-8}  & \(\pm 17.22\) & \(\pm 6.25\) & \(\pm 6.64\) & \(\pm 6.50\) & \(\pm 15.11\) & \(\pm 14.72\) & \(\pm 27.38\) & \(\pm 32.25\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: A machine learning modeling-based period comparisons: real data 12-day periods during (2 October 2019–13 October 2019) and after (14 October 2019–25 October 2019) the strike compared to the same periods modeled as business-as-usual conditions. Negative values show a pollution reduction from the BAU scenario, while positive values show an increase from the BAU scenario. The color scale is based on a gradient color in red, yellow, and green. The greener is the cell, the higher is the pollution drop. Figure 3: Concentrations of trace gases (NO\({}_{2}\), SO\({}_{2}\), CO and O\({}_{3}\)) in Quito averaged (black solid line) over seven monitoring sites (grey shadow) during 12-day periods of pre-strike (20 September 2019–1 October 2019), during the strike (2 October 2019–13 October 2019) and post-strike (14 October 2019–25 October 2019). The red dotted line shows machine learning modeled business-as-usual (BAU) conditions for the city. Error bars show one standard deviation of the observed data. approach. Meanwhile, SO\({}_{2}\) concentrations show a much larger reduction using the ML model (\(-17.65\pm 27.38\%\)) when compared to the inter-period analysis (\(-7.20\pm 7.48\%\)). After the strike, the only pollutant that both methods fully agree on is a continuous reduction of O\({}_{3}\) concentrations (inter-period: \(-25.77\pm 12.01\%\) vs. ML model: \(-21.49\pm 14.72\%\)). Again, the ML model is more conservative, suggesting a possible overestimation of the observational approach.