# A Traffic-Based Method to Predict and Map Urban Air Quality
## 3 Results and Discussion
### Traffic Data Validation and Relationship with PM\({}_{2.5}\) Concentrations  
A thorough analysis of the real traffic and the available traffic applications is displayed in Figure 3. The analysis of the measured vehicle velocities, while driving around the city, and the Google Traffic category shows a negative correlation (R = \(-0.56\)) between an increased velocity and a lightness of traffic (Figure 3a). A negative correlation (R = \(-0.74\)) was also found between the traffic velocity reported in the Waze application and Google Traffic congestion (Figure 3b). The correlations between data from Google Traffic/Waze and actual vehicle velocities suggest that these web-based applications are reliable estimators of the real-time traffic speed in the city of Quito.  
Figure 3: Linear correlation analysis of (**a**) real vehicle velocity measurements and traffic category in Google Traffic and (**b**) traffic velocity in Waze and traffic category in Google Traffic. Where Green represents fast-flowing traffic, Yellow—slower traffic with more vehicles, Red—more congested traffic, Dark red—the most congested or stopped traffic.  
Figure 2: Average (2017–2018) PM\({}_{2.5}\) concentrations in Quito for: (**a**) morning rush hours (06:00–11:59), (**b**) afternoon (12:00–16:59), (**c**) evening rush hours (17:00–20:59), and (**d**) night (21:00–05:59), as shown by the IDW interpolation among monitoring stations.  
Throughout the study, based on tens of hours of sampling (Figure 4a), visual observations confirmed that traffic reported by the Google Traffic application was highly accurate for the main avenues, but not always representative in the secondary residential streets. In several cases of sampling small streets, although the street had no traffic, the traffic application indicated congestion (Google Traffic--Red). This could be due to the parked or stopped cars idling on the side of the street. Based on this finding, we decided to focus our study on the main avenues avoiding secondary residential streets. It is not a significant limitation, for the health exposure to traffic-based pollution, because main avenues represent the principal sources of pollution in a city, as they contain the most polluting city transportation, such as diesel-powered bus lines in the case of Quito. In our study, we also show that the main avenues are highly representative of traffic issues, and most of our sampling data compares well with typical traffic conditions (Figure 4b).  
PM\({}_{2.5}\) concentrations plotted against Google Traffic velocity categories showed an inverse correlation between vehicle velocities and PM\({}_{2.5}\) concentrations until a certain critical level of congestion of the traffic (Dark red, Figure 5a). It can be seen that Green--the fast-flowing traffic--and Dark red--the very slow or stopped traffic--tend to generate less PM\({}_{2.5}\) pollution, due to a limited acceleration and braking when compared to Yellow and Red--the slower traffic (more braking and acceleration). The data represented in Figure 5a were used to create a hierarchical cluster analysis in the form of a dendrogram. On the x-axis are the variables (traffic levels). Similar variables are joined by lines whose vertical length reflects the Euclidean distance between these variables. The dendrogram shows that Green and Dark red are grouped as a single class that results in lower concentrations of fine particulate matter, whereas Yellow and Red constitute another class producing higher levels of particulate pollution (Figure 5b). Based on these findings, the traffic feature was divided into two categories: group 1 (Green + Dark red) and group 2 (Yellow + Red).  
Figure 4: Study area maps: (**a**) the paths in the study perimeter for different dates of measurements; (**b**) real traffic measurements based on the register of Google Traffic application for 3 June 2019, overlapped with the typical traffic in central Quito.