## 3 Results and Discussion
### Assessment of the Methods
The other three pollutants show a continuous reduction in concentrations using the ML model approach (NO\({}_{2}\): \(-12.43\pm 6.25\%\); CO: \(-4.29\pm 6.50\%\); SO\({}_{2}\): \(-9.71\pm 32.25\%\)) and nearly no changes using the inter-period analysis (NO\({}_{2}\): \(-2.79\pm 5.82\%\); CO: \(1.62\pm 5.83\%\); SO\({}_{2}\): \(3.29\pm 13.56\%\)). This again might propose the importance of using an ML BAU condition prediction to better estimate the extended effect of the protest on urban air quality. In order to confirm which of the two approaches is more accurate, a method performance evaluation is presented in Table 3. It can be observed that the ML model, using the GBM method, demonstrates better performance in RMSE indicators in all cases except for a slightly worse performance on SO\({}_{2}\). For this latter pollutant, the observational inter-period analysis tends to overperform the GBM (except the northern suburban S2-Cotocollao site), even if the difference is minuscule. This result is consistent with previous studies showing a lower performance of the models to predict SO\({}_{2}\) than other pollutants [25; 33; 44]. It suggests that the ML model should integrate additional features to improve the prediction of this particular contaminant [45]. For instance, gas-particle interaction must be taken into consideration in predicting this pollutant [46]. While the meteorology has been accounted for, the PM levels were not considered in the ML prediction for SO\({}_{2}\). A previous study [15] showed large spikes in PM\({}_{2.5}\) concentrations during the strike (beyond the BAU conditions) in most of the sites. Some exceptions were observed in the northern sites (S2-Cotocollao and S3-Belisario). This might help explain a poorer ML performance to predict this pollutant, since the unusual PM behavior, due to barricade burning, is not taken into account. Opposite to the SO\({}_{2}\), the RMSE for NO\({}_{2}\), CO and O\({}_{3}\) computed for the ML modeling approach are considerably smaller than the inter-period method. This outcome suggests that the machine learning method based on weather normalization tends to be more reliable than a simple inter-period comparison to estimate the effect of protests on urban gaseous pollution. However, we do not notice a large difference in the estimate of concentration change between the two methods (see Tables 1 and 2). This apparent contradiction can be explained by the fact that the protest occurred in quite a short period of time, for which the environmental condition (e.g., seasonal variation) did not change significantly. It suggests that a simple averaging approach can provide a fair estimate of pollution change on a few weeks of unchanged meteorology, but might have more complications to account for an event that lasts over several months, such as the COVID-19 lockdown [25]. Ultimately, besides assessing the impact of a protest on urban air quality, the general purpose of this study was to propose a machine learning approach to quantify the effect of an unusual event on urban pollution. This was done by investigating the pollution concentrations during the business-as-usual conditions in an absence of a strike. There are different ways to calculate the BAU conditions. In recent studies focused on the COVID-19