## 4 Discussion
(**a**) Time series of N1 (NO\({}_{2}\)) non-corrected and reference measurements, (**b**) Scatterplot of N1 (NO\({}_{2}\)) corrected and reference measurements. Figure 22: Time-series and scatter plots of NO\({}_{2}\) corrected measurements of node N1 and reference measurements, for the time period from 1 December 2021 to 31 December 2021. (**a**) Time series of N1 (NO\({}_{2}\)) corrected and reference measurements, (**b**) Scatterplot of N1 (NO\({}_{2}\)) corrected and reference measurements. Observing the results, the improvement does not appear directly from the degree of correlation between the corrected data and the reference data, but from the shift of the  
Figure 23: Time-series and scatter plots of O\({}_{3}\) non-corrected measurements of node N1 and reference measurements for the time period from 1 December 2021 to 31 December 2021. (**a**) Time series of N1 (O\({}_{3}\)) non-corrected and reference measurements, (**b**) Scatterplot of N1 (O\({}_{3}\)) non-corrected and reference measurements. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Period**} & \multicolumn{6}{c}{**14 April–13 May 2021**} & \multicolumn{6}{c}{**14 May–30 July 2021**} & \multicolumn{6}{c}{**1–31 December 2021**} \\ \hline \multicolumn{2}{c}{\multirow{2}{*}{**Status**}} & \multicolumn{4}{c}{**Plain**} & \multicolumn{4}{c}{**NLR**} & \multicolumn{4}{c}{**Plain**} & \multicolumn{4}{c}{**NLR**} & \multicolumn{4}{c}{**Plain**} & \multicolumn{4}{c}{**NLR**} \\ \cline{3-14}  & \(\mathbf{a}\) & \(\mathbf{b}\) & \(\mathbf{\mathrm{R}^{2}}\) & \(\mathbf{a}\) & \(\mathbf{b}\) & \(\mathbf{\mathrm{R}^{2}}\) & \(\mathbf{a}\) & \(\mathbf{b}\) & \(\mathbf{\mathrm{R}^{2}}\) & \(\mathbf{a}\) & \(\mathbf{b}\) & \(\mathbf{\mathrm{R}^{2}}\) & \(\mathbf{a}\) & \(\mathbf{b}\) & \(\mathbf{\mathrm{R}^{2}}\) & \(\mathbf{a}\) & \(\mathbf{b}\) & \(\mathbf{\mathrm{R}^{2}}\) \\ \hline \multirow{4}{*}{NO\({}_{2}\)} & N1 & 0.56 & 13.2 & 0.43 & 1.02 & 7.4 & 0.43 & 0.55 & 13.3 & 0.20 & 0.65 & 13.5 & 0.20 & 0.71 & 13.6 & 0.30 & 0.79 & 11.2 & 0.30 \\  & N2 & 0.54 & 13.2 & 0.43 & 0.98 & 7.8 & 0.42 & 0.46 & 11.8 & 0.19 & 0.56 & 12.1 & 0.12 & 0.74 & 5.7 & 0.38 & 0.83 & 3.2 & 0.38 \\  & N3 & 0.50 & 16.4 & 0.33 & 0.92 & 11.2 & 0.33 & 0.55 & 13.0 & 0.17 & 0.64 & 13.3 & 0.17 & 0.67 & 14.9 & 0.36 & 0.77 & 12.5 & 0.36 \\ \hline \multirow{4}{*}{O\({}_{3}\)} & N1 & 0.96 & 5.4 & 0.61 & 1.05 & 4.8 & 0.65 & 0.61 & 16.4 & 0.61 & 0.73 & 4.8 & 0.65 & 0.82 & 6.1 & 0.33 & 0.80 & 6.3 & 0.33 \\  & N2 & 0.88 & 7.8 & 0.65 & 0.97 & 1.3 & 0.68 & 0.52 & 17.7 & 0.56 & 0.6 linear coefficients, after the application of nonlinear regression, which influence the effect of the correction. As a validation of the reliability of the correction, it is worth mentioning the results in the evaluation of the nonlinear regression method through the RMSE, MAD and MAE methods. The RMSE method showed small deviation between the primary and corrected values for both nitrogen dioxide and ozone sensors, as indicated by the tables for the monthly and weekly analysis of the results. In the MAD and MAE methods, the improvement is evident according to the results table. This correction achieved by the application of nonlinear regression has a direct effect on refining the measurements to be closer to the actual ones, which is achieved as demonstrated, for weekly measurements in particular, by Tables 1 and 2, and Figure 5 for ozone, and Tables 3 and 4, and Figure 13 for nitrogen dioxide, as well as by Figures 7 and 8 for ozone and Figures 15 and 16 for nitrogen dioxide, on a monthly basis. According to scaling methods, both time and seasonality (Table 9), the application of nonlinear regression, while not improving the correlation coefficient (R\({}^{2}\)), improves the linear coefficients. For both types of low-cost gas sensors (ozone and nitrogen dioxide), the analysis of the corrected measurements is carried out in accordance with, Table 2 and Figure 7 for ozone, and Table 4 and Figure 16 for nitrogen dioxide.