## 5 Discussion  
The application of the LASSO regression method for determining the correction factor to measurement data from low-cost air quality sensors, in particular electrochemical NO\({}_{2}\) and O\({}_{3}\) sensors, has been shown to improve the measurements from these sensors.  
The experiment was carried out on one month's data. For NO\({}_{2}\), the corrected measurements with the correction factor derived from the LASSO regression compared to the noncorrected measurements showed an improvement in the correlation coefficient (R\({}^{2}\)) of  
Figure 27: Time series and scatter plots of the O\({}_{3}\) corrected measurements of three low-cost sensors (N1, N2, N3) and reference measurements, in the time period of 1 December 2021 to 31 December 2021. (**a**) Time series of N1 (O\({}_{3}\)) corrected and reference measurements; (**b**) Scatterplot of N1 (O\({}_{3}\)) corrected and reference measurements; and reference measurements; (**c**) Time series of N2 (O\({}_{3}\)) corrected and reference measurements; (**d**) Scatterplot of N2 (O\({}_{3}\)) corrected and reference measurements; (**e**) Time series of N3 (O\({}_{3}\)) corrected and reference measurements; (**f**) Scatterplot of N3 (O\({}_{3}\)) corrected and reference measurements.  
up to 4%, while the linear coefficient in the scatter plots of the corrected measurements in relation to the reference measurements shows a value closer to one (1). For O3, the corrected values using LASSO regression showed that the degree of correlation (R\({}^{2}\)) could be improved by up to 2%, while the linear coefficient within the scatter plots of the corrected measurements in relation to the reference measurements also shows for these sensors a value closer to one (1) than for the noncorrected measurements. Although both remote and global calibration approaches promise scalability and robustness to deviation, the LASSO regression method shows satisfactory results with minimal computational power.  
Although for both types of low-cost sensors, the correlation coefficient (R\({}^{2}\)) does not show a significant improvement, the linear coefficient contributes to the improvement of the measurements, as the corrected measurements are very close to the reference measurements, resulting in more realistic and trustworthy measurements. This observation occurs both for the time scaling in the month of the experiment (April 2021) and in the summer months (May, June 2021), as well as the seasonality scale between the summer months and the winter month (December 2021), which means that the correction with the LASSO regression method is not affected by time extension or seasonality.  
To evaluate the correction factor using the LASSO regression method, the MAD, MAE, and RMSE methods were applied to both noncorrected and corrected measurements. Tables 5 and 6 for NO2 and O3, respectively, show that, for NO2, the values of the MAD, MAE and RMSE methods between corrected and noncorrected values show very small deviations which do not significantly affect the result. For O3, the RMSE method showed very little divergence between the noncorrected and corrected measurements, while in the MAD and MAE methods the improvement is evident according to the results in Table 6. This correction achieved by the correction factor, which was identified by applying the LASSO regression, has the direct effect of improving the measurements to be closer to the real ones.