## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Evaluation Scenario**} & \multicolumn{4}{c}{**Pollutant**} \\ \cline{3-7}  & \multicolumn{2}{c}{**NO2**} & \multicolumn{2}{c}{**PM10**} \\ \hline  & **Train Set** & **Test Set** & **R2** & **RMSE (\(\mathbf{\mu}\)g/m3)** & **R2** & **RMSE (\(\mathbf{\mu}\)g/m3)** \\ \hline \multirow{3}{*}{**LR**} & 2.3–16.3.**2019**. & 16.3–6.5.**2019**. & 0.728 & 11.416 & 0.615 & 15.644 \\  & 2.3–16.3.2020. & 16.3–6.5.**2020**. & 0.260 & 14.487 & 0.699 & 14.903 \\  & 2.3–16.3.2021. & 16.3–6.5.**2021**. & 0.601 & 13.061 & 0.299 & 16.689 \\ \hline \multirow{3}{*}{**ANN**} & 16.3–6.5.2019**. & 16.3–6.5.**2020**. & 0.606 & 9.123 & 0.741 & 13.950 \\  & 16.3–6.5.**2020**. & 16.3–6.5.**2021**. & 0.614 & 11.292 & 0.438 & 23.396 \\  & 16.3–6.5.**2019**. & 16.3–6.5.**2021**. & 0.652 & 10.460 & 0.157 & 18.185 \\  & 16.3–6.5. & 16.3–6.5.**2021**. & 0.671 & 10.555 & 0.255 & 19.211 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Evaluation metrics for different train/test scenarios. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**CO**} \\ \hline  & **Ref. ** & **Ref. ** & **Ref. ** & **LR** & **LR** & **LR** \\
**Year** & **Mean** & **Median** & **Std** & **Mean** & **Median** & **Std** \\
**(mg/m3)** & **(mg/m3)** & **(mg/m3)** & **(mg/m3)** & **(mg/m3)** & **(mg/m3)** & **(mg/m3)** \\ \hline
2019 & 0.33 & 0.28 & 0.18 & 0.35 & 0.30 & 0.17 \\
2020 & 0.64 & 0.60 & 0.13 & 0.72 & 0.67 & 0.15 \\
2021 & 0.50 & 0.43 & 0.18 When it comes to the NO\({}_{2}\) measurements, quite a significant decline in the mean, median and standard deviation values can be observed in 2020 for both the reference and low-cost sensors. This is also consequence of the state of emergency, since the main source of NO\({}_{2}\) is traffic, which was significantly reduced during the state of emergency. The low values of NO\({}_{2}\) are also responsible for the poor correlation between the reference and LR data (R2 factor of 0.260). The low-cost NO\({}_{2}\) sensor tends to have trouble with low concentrations of NO\({}_{2}\), but the implemented ANN managed to pick up on these dependencies as well as the influence of RH and t on the measurements. Furthermore, the ANN trained on 2019 data that was applied to 2020 data shows a significant improvement of the raw measurements, implying that the behavior of the NO\({}_{2}\) sensor, although not perfect, is consistent through 2019 and 2020. The achieved improvement can be seen in Figures 2 and 3, with Figure 2 containing the results of the LR performed in 2020 and Figure 3 containing the results of the ANN trained on 2019 data and applied to 2020 data. Figures 2 and 3 show not only a clear improvement in regard to the scattered points being closer to the \(y=x\) line, but the histogram shape as well. The consistent behavior of the low-cost NO\({}_{2}\) sensor can also be observed in 2021. The initial correlation (after only the LR correction) is a lot better than in 2020, since the traffic (and therefore NO\({}_{2}\) concentration) was closer to the regular regime. The improvement achieved using the ANN is still present, but less prominent than in 2020. It is also important to note that a better improvement was achieved when the ANN was trained on the data from both 2019 and 2020, as opposed to the case where the training set contained only 2020 data. This is an expected result because the low-cost NO\({}_{2}\) sensor seems to have consistent behavior throughout the years, and a larger training dataset implies a better correspondence of the algorithm with the underlying dependencies. The achieved results can be seen in the following figures, with Figure 4 showing the results of the LR performed in 2021 and Figure 5 showing the results of the ANN that was trained on 2019 and 2020 data. Higher concentrations of the NO\({}_{2}\) measurements can also be seen in the figures in comparison to the year 2020.