# A Traffic-Based Method to Predict and Map Urban Air Quality
## 3 Results and Discussion
### Models and Prediction Accuracy  
#### 3.3.1 PM\({}_{2.5}\) Prediction from Monitoring Stations and IDW  
To study the prediction power of urban pollution based on monitoring stations, we verified the IDW interpolation data with the real measurements. We performed a sensitivity analysis on different p values (range from 1 to 5) to identify the power that provides the best prediction. Overall, the difference of accuracy from one p to another is not significant (Table 1). Nevertheless, the remaining analyses will focus on the models using \(p=2\), because they tend to give the best performance.  
Two out of four models do not provide a prediction which is significantly different from a random choice between low and high concentration of PM\({}_{2.5}\). The percentage of accuracy of the models for 30 May and 18 June, 2019 are both equal to 50%. However, 30 May, 2019 was a day with high relative humidity, low solar radiation, and thus low temperature, which may increase a variability in local source mixing (Figure A1e, Appendix A). On the other hand, 18 June, 2019 was warmer and, thus, windier (Figure A1h, Appendix A). For 12 June, 2019, increased cloudiness caused larger temperature changes (Figure A1g, Appendix A), therefore, the model is slightly better (accuracy = 57%) but still below the expectations (Figure 6a). The only model that provides us with good prediction is 3 June, 2019 (Figure 6f): accuracy = 71%. This day had relatively constant meteorological conditions during the measurements, pointing to the less variation in time of the air pollution. Figure A1b,f demonstrates the relationship between peak PM\({}_{2.5}\) concentrations and a decrease in wind speed. Finally, Figure A1a-d (Appendix A) show that the evolution of the concentrations of fine particulate matter over time is quite different between the measurements at the station and the street level for the days 30 May, 2019, and 12 and 18 June, 2019. The levels registered by the monitoring stations for these days (several small  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(p\) Values & \(p\) = 1 & \(p\) = 2 & \(p\) = 3 & \(p\) = 4 & \(p\) = 5 \\ \hline May 30 th & 50\% & 50\% & 50\% & 50\% & 49\% \\ June 3 rd & 67\% & 71\% & 70\% & 70\% & 70\% \\ June 12 th & 57\% & 57\% & 57\% & 57\% & 57\% \\ June 18 th & 50\% & 50\% & 50\% & 50\% & 50\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparing the prediction accuracy for different powers (\(p\)).  
Figure 5: Google traffic data analysis (Green—fast-flowing traffic, Yellow—slower traffic with more vehicles, Red—more congested traffic, Dark red—the most congested or stopped traffic): (**a**) Correlation analysis between average PM\({}_{2.5}\) concentrations and Google Traffic categories; (**b**) Dendrogram from the cluster analysis of the traffic based on PM\({}_{2.5}\) concentrations. The clustering splits the data of the four levels of traffic into two most representative classes, showing that Green and Dark red can be grouped, whereas Yellow and Red compose another independent class. Height reflects the distance between the levels (the clusters are defined by choosing a cutoff distance).  
peaks) are significantly noisier than for 3 June, 2019 (clear pollution peak at 9:20). These results tend to demonstrate that the use of the air quality network provides limited spatial resolution and can only be suitable in the case of typical days. For a more reliable prediction, another approach less dependent on the meteorological conditions needs to be adopted. The best way to reduce the effect of the meteorology is to, directly or indirectly, measure the pollution closer to its source of emission. This is the approach that consists of monitoring traffic, for which the resulting models are presented in the next sections.