## 3 Results and Discussion  
Table 2 shows the evaluation metrics for the performed measurement corrections for NO\({}_{2}\) and PM\({}_{10}\). The agreement between the raw and reference CO measurements is initially quite high for each year (R2 ranging from 0.833 to 0.894), and no significant improvement is achieved for this pollutant using the correction of the raw measurements described in the previous section. Because of this, results regarding the CO measurements are not shown in Table 2.  
The presented results show that, for NO\({}_{2}\), every train/test scenario for the ANN shows an improvement over the simple LR correction results. The most prominent improvement for NO\({}_{2}\) is achieved when the ANN is trained on data from 2019 and evaluated on data from 2020. It is also notable that the results for the test year 2021 are better when data from both 2019 and 2020 are used as the training set. When it comes to the results for the PM\({}_{10}\), the most prominent improvement is present when the ANN is trained on 2019 data and tested on 2020 data. The R2 factor for PM\({}_{10}\) is also improved for 2021, but only when the ANN is trained on 2020 data, and there seems to be an increase in the RMSE parameter. All of these observations can be explained when taking into consideration the nature of the sensors alongside the values of the measured pollutants and weather parameters. The mean, median and standard deviation values are shown in Table 3, for each year and each measured variable. In the remainder of this section, we explain in more detail the observations made for the results shown in Table 2. \begin{table}
\begin{tabular}{l l} \hline \hline \multicolumn{1}{c}{**Layer Type**} & \multicolumn{1}{c}{**Layer Properties**} \\ \hline Conv1D & filters = 64, kernel size = 3, strides = 1 \\ Conv1D & filters = 64, kernel size = 3, strides = 1 \\ Batch normalization & / \\ Max pooling & pool size = 2, strides = 2 \\ Dropout & drop rate = 0.5 \\ Conv1D & filters = 128, kernel size = 3, strides = 1 \\ Conv1D & filters = 128, kernel size = 3, strides = 1 \\ Batch normalization & / \\ Max pooling & pool size = 2, strides = 2 \\ Dropout & drop rate = 0.5 \\ Dropout & drop rate = 0.5 \\ Conv1D & filters = 256, kernel size = 3, strides = 1 \\ Batch normalization & / \\ Max pooling & pool size = 2, strides = 2 \\ Dropout & drop rate = 0.5 \\ Conv1D transpose & filters = 256, kernel size = 3, strides = 1 \\ Batch normalization & / \\ Conv1D transpose & upsampling factor = 2 \\ Dropout & drop rate = 0.5 \\ Conv1D transpose & filters = 128, kernel size = 3, strides = 1 \\ Batch normalization & / \\ Upsampling & upsampling factor = 2 \\ Dropout & drop rate = 0.5 \\ Conv1D transpose & filters = 64, kernel size = 3, strides = 1 \\ Batch normalization & / \\ Upsampling & upsampling factor = 2 \\ Dropout & drop rate = 0.5 \\ Conv1D transpose & filters = 1, kernel size = 3, strides = 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Convolutional network architecture. The values given in Table 3 can explain many phenomena regarding the influence of the state of emergency, as well as provide an explanation for the results shown in Table 2.  
Firstly, let us observe the measurements of CO. The values of the mean and median values of the CO reference measurements are the highest in 2020. This can be explained by the larger number of people staying indoors, because of the state of emergency, in comparison to 2019 and 2021. The observed period in each year includes the heating season, which, in addition to the larger number of people staying indoors in 2020, could explain the observable increase in the CO measurements.