# A Traffic-Based Method to Predict and Map Urban Air Quality
## 3 Results and Discussion
### Models and Prediction Accuracy
#### 3.3.3 PM\({}_{2.5}\) Prediction from Traffic and Time of the Day  
Including time of the day in the models improves the accuracy by an average of 6.5% (mean performance of traffic-based only = 65.5%; mean performance of traffic + time of the day = 72%). Here, all the predictions are higher or equal to 70%. Three out of four models split first on time of day (Figure 8a,c,d), which means that the temporal factor is a dominant feature for the estimation of the pollution levels. The four models show that the earlier the time, the higher the PM\({}_{2.5}\) concentration is. Two main reasons can explain this outcome. First, rush hour occurs before 9:00. Since the traffic is denser during this period, the emission of particulate matter increases. The second explanation is  
Figure 6: Decision tree models to predict the levels of contamination by PM\({}_{2.5}\) (high vs. low) according to an IDW interpolation of the concentrations measured at the four monitoring stations (Belisario, Centro, Cottocollao, and El Camal). The two models correspond to the days as follows: (**a**) 12 June, 2019 and (**b**) 3 June, 2019. The numbers indicate the cutoff values (in \(\upmu\)g m\({}^{-3}\)) that permit the best split on the predictor (i.e., IDW) to separate low and high concentrations of PM\({}_{2.5}\).  
related to the height of the planetary boundary layer (PBL). PBL is low in the early morning and keeps growing all morning long, due to the intensification of solar radiation, which, in result, increases the dilution of PM\({}_{2.5}\) in the atmosphere [36], especially after 10:00 (Figure 8b). These two phenomena account for the two thresholds (around 9:00 and 10:00) for which the time feature is split in the models presented in Figure 8. Regarding the split for the traffic feature, the models present similar thresholds as in the previous section. A traffic type 1 (fluid or completely congested) is a predictor of less contamination than a traffic type 2 (slower flow). To sum up, adding the time of day enables the weaker models solely based on traffic to get a similar performance as the best models (prediction \(\geq\) 70%), but it does not significantly improve the accuracy of these latter.  
Figure 8: Decision tree models to predict the levels of concentration of PM\({}_{2.5}\) (high vs. low) according to both the intensity of the traffic and time of the day. The four models correspond to the days as follows: (**a**) 30 May, 2019, (**b**) 3 June, 2019, (**c**) 12 June, 2019, and (**d**) 18 June, 2019. The numbers indicate the cutoff values (hours:minutes of the day for Time; and scale of flow for Traffic) that permit the best split on the predictors to separate low and high concentrations of PM\({}_{2.5}\).  
Figure 7: Decision tree models to predict the PM\({}_{2.5}\) pollution levels (high vs. low) according to the intensity of the traffic in the city. The traffic values are calculated from the groups defined by the clustering analysis (see Section 3.2.) and range from 1 (fast or completely stopped) to 2 (significant reduction of the vehicle flow). The three models correspond to the days as follows: 30 May, 2019 (a—solid line), 3/18 June, 2019 (b—broken line), and 12 June, 2019 (c—dashed line). The numbers indicate the cutoff values (in scale of traffic) that permit the best split on the predictor (i.e., traffic) to separate low and high concentrations of PM\({}_{2.5}\).