# A Comparative Analysis for Air Quality Estimation from Traffic and Meteorological Data
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Pollutant**} & \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**w = 0**} & \multicolumn{2}{c}{**w = 1**} & \multicolumn{2}{c}{**w = 2**} \\ \cline{3-8}  & & **RMSE** & **sMAPE** & **RMSE** & **sMAPE** & **RMSE** & **sMAPE** \\ \hline \multirow{4}{*}{\(PM_{10}\)} & LinBR & 20.8109 & 0.2079 & 19.3394 & 0.1961 & 19.2768 & 0.1944 \\  & BRNN & **18.0258** & **0.1656** & 20.0401 & 0.1786 & 19.4796 & 0.1720 \\  & RF & 18.1356 & 0.1795 & **17.8104** & **0.1736** & **18.3169** & **0.1702** \\  & LSTM & 20.2155 & 0.2031 & 20.6779 & 0.1945 & 20.7596 & 0.1976 \\ \hline \multirow{4}{*}{\(PM_{2.5}\)} & LinBR & 15.2088 & 0.2047 & 14.3527 & 0.1947 & 14.2678 & 0.1883 \\  & BRNN & **14.8256** & 0.2102 & 14.2252 & 0.1976 & 14.6765 & 0.1867 \\ \cline{1-1}  & RF & 14.8692 & **0.1944** & **12.7866** & **0.1658** & **13.4144** & **0.1691** \\ \cline{1-1}  & LSTM & 16.1347 & 0.2219 & 14.5807 & 0.1962 & 15.6619 & 0.2107 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Results of the selected models on the Daily Set. The results are again presented with RMSE and sMAPE on different time windows, corresponding to no lag, one-day lag and two-days lag. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \(PM_{10}\) & \(PM_{2.5}\) & \(NO_{2}\) & \(NO_{x}\) & \(C_{b}H_{6}\) & \(CO\) & \(BC\) & \(O_{3}\) \\ \hline min & 5.667 & 1.000 & 8.050 & 0.000 & 0.100 & 0.375 & 0.100 & 0.500 \\ max & 156.000 & 134.500 & 203.971 & 1111.367 & 12.433 & 4.100 & 22.554 & 206.250 \\ mean & 35.363 & 26.967 & 51.501 & 107.104 & 1.668 & 1.109 & 3.004 & 42.806 \\ std & 21.222 & 18.596 & 23.868 & 97.418 & 1.371 & 0.465 & 2.634 & 36.957 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Descriptor values for target variables. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**w = 0**} \\ \hline model\textbackslash{} pollutant & \(NO_{2}\) & \(O_{3}\) & \(PM_{10}\) & CAQI & CAQI* \\ \hline LinBR & 0.6712 & 0.8672 & 0.4304 & 0.1352 & 0.6598 \\ BRNN & 0.6703 & 0.7533 & 0.4933 & 0.1275 & 0.6630 \\ RF & 0.7427 & 0.9272 & 0.4787 & 0.0889 & 0.7092 \\ LSTM & 0.6113 & 0.8908 & 0.4727 & 0.1789 & 0.5261 \\ \hline \hline \multicolumn{4}{c}{**w = 24**} \\ \hline model\textbackslash{}pollutant & \(NO_{2}\) & \(O_{3}\) & \(PM_{10}\) & CAQI & CAQI* \\ \hline LinBR & 0.6418 & 0.9139 & 0.8351 & 0.8242 & 0.6598 \\ BRNN & 0.5991 & 0.8840 & 0.1100 & 0.1260 & 0.5971 \\ RF & 0.7312 & 0.9157 & 0.1117 & 0.1194 & 0.7092 \\ LSTM & 0.7291 & 0.9197 & 0.1213 & 0.2207 & 0.7011 \\ \hline \hline \multicolumn{4}{c}{**w = 48**} \\ \hline model\textbackslash{}pollutant & \(NO_{2}\) & \(O_{3}\) & \(PM_{10}\) & CAQI & CAQI* \\ \hline LinBR & 0.7352 & 0.8902 & 0.5478 & 0.6256 & 0.6928 \\ BRNN & 0.6029 & 0.8485 & 0.5166 & 0.1806 & 0.5720 \\ RF & 0.7469 & 0.9207 & 0.5181 & 0.6130 & 0.7132 \\ LSTM & 0.7145 & 0.9234 & 0.5723 & 0.7882 & 0.6866 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: F1-scores obtained on the sub-indices of each pollutant, on the global indices CAQI and CAQI* (without \(PM_{10}\)), using the same configurations with time windows equal to zero, one and two days. The sub-indices are consistent with the performances described in the regression task: \(I_{\text{NO}_{2}}\) reached on average affordable F1-score, with the Random Forest obtaining the best score around 0.75, while \(O_{3}\) was best predicted by the LSTM with F1-Score of 0.92. Reflecting the inferior regression performances on the previous analysis, worse performances were instead achieved on \(I_{PM_{10}}\) by every model in most cases. A few exceptions comprise the models trained on the 48-h window, where the F1-scores on average exceed the 0.5 threshold, and the linear model on the 24-h configuration. This is again expected, as the lack of data negatively influenced the regression estimates, thus undermining the classification results as well.