# A Traffic-Based Method to Predict and Map Urban Air Quality
## 4 Conclusions  
In this first-of-its-kind study, we investigated different ways to predict and map street-level urban air pollution. We used machine learning techniques and Inverse Distance Weighting (IDW) on real measurements of fine particulate matter (PM\({}_{2.5}\)-aerodynamic diameters \(\leq\) 2.5 \(\upmu\)m). Firstly, urban PM\({}_{2.5}\) concentration mapping, based on the air quality network of Quito, Ecuador, showed that the resolution of spatial variation of the air pollution is relatively low. While this method is important for representing the general air quality conditions for the city, it is not adequate to estimate the exposure of the urban population to street-level air pollution. Therefore, in this study, we propose an innovative way to model urban PM\({}_{2.5}\) on the basis of traffic intensity. To confirm the suitability of available traffic \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Models** & **Traf\_a** & **Traf\_b** & **Traf\_c** & **Traf\_Tim\_a** & **Traf\_Tim\_b** & **Traf\_Tim\_c** & **Traf\_Tim\_d** \\ \hline Within-cluster & & & & & & \\ squared & 15.71 & 14.21 & 20.3 & 93.6 & 137.12 & 145.35 & 99.97 \\ distance & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparing the different models in terms of overall average within-cluster distance. The first three models are the ones obtained from traffic (columns 2–4). The last four models are the ones built from traffic and the time of day (columns 5–8). Figure 10: Real-time traffic (**a**) and a model based on traffic for prediction of PM\({}_{2.5}\) (**b**) for a larger (6.5 km \(\times\) 5.5 km) area in central Quito. applications, we compared traffic data provided by Google Traffic and Waze to the actual traffic speed measurements. Then, we performed a correlation study between the measured traffic and real-time PM\({}_{2.5}\) concentrations, that helped us to split the data into two categories of high and low concentrations for slow traffic (increased acceleration and braking) and fluid or stopped traffic (reduced acceleration and braking), respectively. To study the prediction power of every method, we verified the Inverse Distance Weighting interpolation data with the real measurements. The interpolation of the monitoring network data exposed limitations and low prediction accuracy (50%-71%), varying from random to improved results for the day with less varying meteorological conditions. The PM model solely based on traffic showed an increased representability of air quality conditions (61%-71% prediction accuracy). Furthermore, our model for PM\({}_{2.5}\) prediction based on traffic and time of the day confirmed that including time in the model tends to improve the accuracy by an average of 6.5%. In the latter case, the temporal factor was a dominant feature for the estimation of pollution levels, confirming that the earlier the time, the higher the PM\({}_{2.5}\) concentration. As the rush hour occurs before 9:00, the traffic is denser during this period and the concentrations of particulate matter increase. In addition, the height of the planetary boundary layer is low in the early morning, which inhibits the dilution of PM\({}_{2.5}\) during the morning hours resulting in the peak concentrations. Finally, we tested the best model on any day, in order to verify the robustness of the proposed approach. Since the accuracy was maintained, we were able to confirm the model generalization based on traffic (accuracy of 61%-64%). We also noted that the models including the time factor do not generalize very well, which suggests that the simplest models are the most robust and the most reliable feature to predict atmospheric pollution is the flow of vehicles in the city. Our finding is confirmed at a larger scale through an assessment based on an unsupervised learning technique.