# A Comparative Analysis for Air Quality Estimation from Traffic and Meteorological Data
## 3 Results
In a similar manner to the Hourly Set, further increasing the interval explored did not translate into better estimates. In the specific case of LSTM, the small dataset with daily resolution was not enough to take advantage of the time series structure and the resulting performance are on par with simpler and faster models. \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 = 24**} & \multicolumn{2}{c}{**w = 48**} \\ \cline{3-8}  & & **RMSE** & **sMAPE** & **RMSE** & **sMAPE** & **RMSE** & **sMAPE** \\ \hline \multirow{4}{*}{\(NO_{2}\)} & LinBR & 23.1846 & 0.1483 & 22.6198 & 0.1455 & 16.3636 & 0.1125 \\  & BRNN & 18.3557 & 0.1383 & 23.8471 & 0.2385 & 27.4220 & 0.3130 \\  & RF & **15.0055** & **0.1090** & **14.3534** & **0.1029** & **14.5001** & **0.1030** \\  & LSTM & 37.7267 & 0.5072 & 16.4886 & 0.1160 & 15.4383 & 0.1172 \\ \hline \multirow{4}{*}{\(NO_{x}\)} & LinBR & 68.6586 & 0.2058 & 64.1705 & 0.1801 & 62.0700 & 0.1670 \\  & BRNN & 80.6203 & 0.2425 & 90.5791 & 0.3007 & 103.2989 & 0.3742 \\  & RF & **66.4126** & **0.1764** & **62.0596** & **0.1599** & 64.2854 & 0.1634 \\  & LSTM & 99.4813 & 0.4536 & 78.0742 & 0.1912 & **58.8296** & **0.1598** \\ \hline \multirow{4}{*}{\(C_{6}H_{6}\)} & LinBR & 1.0836 & 0.2128 & **0.9825** & **0.1887** & 1.0204 & 0.1801 \\  & BRNN & **1.0489** & 0.2119 & 1.3753 & 0.2049 & 1.2660 & 0.2965 \\  & RF & 1.2422 & 0.2652 & 1.1053 & 0.2315 & 1.1259 & 0.2296 \\  & LSTM & 1.0576 & **0.1990** & 1.0272 & 0.1947 & **0.9637** & **0.1768** \\ \hline \multirow{4}{*}{\(CO\)} & LinBR & **0.3930** & **0.1401** & 0.4427 & 0.2118 & 0.4341 & 0.1833 \\  & BRNN & 0.5162 & 0.1788 & 0.6543 & 0.2408 & 0.5529 & 0.2370 \\  & RF & 0.4498 & 0.1524 & **0.4276** & **0.1435** & 0.4426 & 0.1464 \\  & LSTM & 1.2382 & 0.2998 & 0.4647 & 0.1502 & **0.3413** & **0.1110** \\ \hline \multirow{4}{*}{\(BC\)} & LinBR & **2.0829** & 0.2406 & 2.0312 & 0.2221 & 2.0092 & 0.2007 \\  & BRNN & 2.5971 & 0.2944 & 3.1198 & 0.2925 & 2.6993 & 0.3601 \\  & RF & 2.1384 & **0.2325** & **1.8803** & **0.1951** & **1.8913** & **0.1951** \\  & LSTM & 2.2806 & 0.2678 & 2.4090 & 0.2154 & 2.0605 & 0.2274 \\ \hline \multirow{4}{*}{\(O_{3}\)} & LinBR & **15.7620** & **0.2934** & 14.7650 & 0.2411 & 17.9655 & 0.2534 \\  & BRNN & 31.3684 & 0.3989 & 18.1709 & 0.3494 & 22.2982 & 0.5929 \\ \cline{1-1}  & RF & 18.0772 & 0.3364 & 16.2145 & 0.3016 & 15.6594 & 0.2881 \\ \cline{1-1}  & LSTM & 23.0364 & 0.4558 & **14.4157** & **0.2402** & **14.3838** & **0.2264** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Results of the adopted models on the Hourly Set. For each pollutant and model, RMSE and sMAPE metrics are provided, in three different scenarios: no lag, 24-h, and 48-h windows. Results highlighted in bold represent the best performing model w.r.t the pollutant and the window \(w\). Given the regression estimates and their performances described in previous paragraphs, we analysed the results on the AQI classification task. In Table 9 the F1-scores resulted from the AQI computation over ground-truth and predicted pollutants are summarised, where each subsection refers to the same configuration of time intervals already applied on the previous tasks. In every subsection, the first columns represent a pollutant \(p\) and each row represents a model \(m\). Consequently, each cell contains the F1-score of the Air Quality sub-index (Table 5) obtained from the ground truth of \(p\) and the estimates produced my \(m\), on the same testing period of \(p\). The last two columns of every block summarise instead the global AQI, defined as \(CAQI=max(I_{NO_{2}},I_{O_{3}},I_{PM_{10}})\), where each \(I_{k}\) represents a sub-index.