## 4 Discussion  
The improvements in the methodology of nonlinear regression as correction factor show satisfactory results. In general, simple nonlinear regression models as correction factors in low-cost sensor measurements are not commonly found in the literature. Instead, many research papers have been published on correction models using random forest (RF), artificial neural networks (ANN), support vector machine (SVM), and support vector regression (SVR) techniques. These techniques have shown excellent results in improving measurements. As the platform presented here belongs to IoT devices, it is understood that such technologies are not typically supported by IoT devices. Given that the correction of the measurements has to be carried out at the monitoring station, which is based on an IoT device, with limited computing power, the application of correction by nonlinear regression is feasible, compared to other methods which require high processing power and cannot be underestimated by such devices. The application of simple nonlinear regression (NLR) to measurement data from low-cost sensors, in particular electrochemical nitrogen dioxide and ozone sensors, shows that it can improve the measurements from these sensors to a certain extent. The experiment was conducted using both weekly and monthly data. Concerning ozone, the weekly corrected data exhibited an improvement in the degree of correlation of up to 5% compared to the primary data, while the monthly corrected data showed an enhancement in the correlation coefficient of up to 4%. For nitrogen dioxide, the weekly corrected data demonstrated an increase in the correlation coefficient of up to 9% compared to the primary data, while the monthly corrected data indicated an improvement in the correlation coefficient of up to 2.5%. The validation of the improvements are shows by the comparison results of the RMSE, MAD and MAE methods, According to Tables 5 and 6 comparing the primary and corrected measurements in respect to reference measurements, for the monthly measurements of ozone, the RMSE shows an improvement of up to 60%, the MAD shows an improvement or up to 24% and the MAE shows an improvement of up to 15%. For the weekly measurements of ozone, the RMSE shows an improvement of up to 86%, the MAD shows an improvement of up to 10%, and the MAE shows an improvement of up to 10%. \begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline  & & & & & & & & & \\ \hline
**Sensors** & **N1** & **N2** & **N3** & **N1** & **N2** & **N3** & **N1** & **N2** & **N3** \\ \hline
**Method** & & **RMSE** & & & **MAD** & & & **MAE** & \\ \hline Primary & 0.90 & 1.04 & 1.45 & 2.29 & 3.47 & 2.57 & 11.78 & 12.89 & 10.16 \\ Corrected & 1.16 & 1.12 & 1.50 & 1.43 & 3.18 & 2.36 & 12.28 & 12.36 & 12.77 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Monthly evaluation of NO\({}_{2}\) measurements via RMSE, MAD, and MAE methods. \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & & & & & & & & \\ \hline
**Sensors** & **N1** & **N2** & **N3** & **N1** & **N2** & **N3** & **N1** & **N2** & **N3** \\ \hline
**Method** & & **RMSE** & & & **MAD** & & & & **MAE** & \\ \hline Primary & 1.87 & 2.15 & 3.01 & 1.46 & 3.47 & 1.34 & 7.52 & 10.28 & 4.70 \\ Corrected & 1.93 & 2.20 & 3.04 & 1.42 & 3.38 & 1.33 & 7.33 & 10.00 & 4.54 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Weekly evaluation of NO\({}_{2}\) measurements via RMSE, MAD, and MAE methods. improvement of up to 14% and the MAE shows an improvement of up to 9%. According to the Tables 7 and 8 comparing the primary and corrected measurements in respect to reference measurements, for the monthly measurements of nitrogen dioxide, the RMSE shows an improvement of up to 28%, MAD shows an improvement of up to 60% and MAE shows an improvement of up to 25%. For the weekly measurements for ozone, the RMSE shows an improvement of up to 3%, the MAD shows an improvement of up to 3% and the MAE shows an improvement pf up to 4%.