# A Traffic-Based Method to Predict and Map Urban Air Quality
## 3 Results and Discussion
### PM\({}_{2.5}\) Mapping  
Real measurements and modeling results are presented for 3 June, 2019 at 8:00-10:20 in Figure 9. First, we show the IDW interpolation of PM\({}_{2.5}\) concentrations measured at the neighboring monitoring stations (see Figure 9a). It is clear that the PM\({}_{2.5}\) pollution interpolation of sparsely distributed air quality network stations is simply not good enough to estimate exposure to urban pollution at the local level. We then compare the traffic conditions during the experiment (colored overlapping diamond markers, Figure 9a) and an IDW interpolation of the PM\({}_{2.5}\) concentrations measured that same day during the same hours (Figure 9b). Frequently, increased traffic is a cause of the increase in PM\({}_{2.5}\) concentrations, which can be observed in our study. Meanwhile, the lowest concentrations are registered in the city parks (green areas, Figure 9). Finally, we also compare the real PM\({}_{2.5}\) measurementswith the modeled PM\({}_{2.5}\) based on traffic only (Figure 9c), and traffic + time of the day (Figure 9d). The heat maps are obtained by applying an IDW interpolation method on the results provided by the Decision-Tree model for each road segment. While both models performed well in predicting real PM\({}_{2.5}\) pollution, adding the time of the day to the traffic predictor, insignificantly improved the model from 71 to 73% accuracy. However, time of the day is a crucial parameter accounting for the effect of atmospheric dilution. This might help to better predict the dynamics of PM\({}_{2.5}\) concentrations in any city, which is illustrated in Figure 9. It can be seen that Figure 9d better spatially represents the real pollution (Figure 9b) than Figure 9c. Finally, it is by far much better than just relying on the information of the monitoring stations (Figure 9a). A possible way to further improve this spatial model would be to include the road-type information. In the case of a highway, traffic behavior is very different, and even if the flow is relatively fast (Green), the concentration of the vehicles is high enough to cause a significant increase in PM\({}_{2.5}\) concentrations. Besides, our measurements are based on the sampling at a human step (4 km/h), which might mean that by the time we would reach a congested area, the traffic had already started moving, and we would have to register a fast flow (Green), while a minute ago it was highly congested (Red) and would cause a cloud of pollution in that area. However, if our model would be applied to real-time traffic, the performance would likely be further improved. This suggests a potential power in using this model for real Google Traffic information. Figure 9: Concentrations of PM\({}_{2.5}\) for 3 June, 2019 8:00–10:20, for: (**a**) Quito monitoring station IDW interpolation of the real data from 4 closest monitoring stations and measured traffic intensity; (**b**) street-level measurements in this study, (**c**) model based on traffic only; and (**d**) model based on traffic and time of the day. Finally, we applied our best traffic-based model (3 June, 2019) on a larger (6.5 km \(\times\) 5.5 km) area of Quito (Figure 10). Google traffic data was registered for the main avenues in Quito central area (Figure 10a) during 8:00-10:30 on 19 June, 2019. For practical reasons, the accuracy of this generalization was not verified through a classical supervised learning assessment. Instead, we used a clustering technique which consisted in applying a k-Means algorithm and calculating the within-cluster squared distance for the seven possible models (see Section 2.4.3 for more details). Table 2 shows that the lowest error is obtained for 'Traffic_b', which confirms that the model built from the data collected on 3 June, 2019 is the best to generalize the proposed approach to the whole city. This suggests the benefit of this method for citizen awareness of air quality in urban areas.