## 6 Conclusions  
Air quality in large cities is an important issue as it can affect the health of citizens living in these areas. The development of technology has led to the rapid evolution of low-cost sensors. Inquiry into the trustworthiness and reliability of measurements obtained from low-cost sensors is a subject of research interest. Many research groups have presented, through their work, calibration and correction methods for low-cost sensor measurements to improve the reliability of measurements. In this work, the procedure for correcting measurements using the least absolute shrinkage and selection operator (LASSO) regression method from low-cost sensors, in particular from electrochemical NO2 and O3 sensors, is presented. In particular, the LASSO regression method was used in our experiment to identify a correction factor which was then applied to the measurements of the low-cost sensors to optimize the measurements from them.  
The proposed methodology provides excellent results, as the results have shown that the correction factor identified using the LASSO regression contributes to the correction of measurements in low-cost electrochemical sensors. Although for both types of low-cost sensors (NO2 and O3) the improvement in the degree of correlation is not significant, the R\({}^{2}\) improvement for NO2 of up to 4%, and the R\({}^{2}\) improvement for O3 of up to 2%, it was observed that the linear correlation coefficient improves as it approaches the value of one (1). This resulted in the optimization of measurements from low-cost sensors, as the corrected measurements appear very close to the reference measurements. In addition, the methodology scaling shows excellent results, firstly at the time scaling of the total of two and a half months of corrected measurements by the LASSO regression method, and secondly at the seasonality scale of optimization of correction measurements by LASSO regression both of the summer and winter months. Implementing simplified mathematical processes in correcting the measurements, striking a balance between effort and results, proved to have an excellent performance. The methods MAD, MAE, and RMSE were applied to both uncorrected measurements and corrected measurements with the above mentioned method. The results showed negligible variation under the RMSE method, while for the MAD and MAE methods the corrected measurements show an improvement.  
All field calibration methodologies have limitations arising from factors such as aging, the duration of the time period, specific environmental conditions, and seasonal variations. The proposed methodology can represent the core method of an adaptive methodology well using remotely available data and recalibration strategies. Extending this study could involve incorporating numerous cost-effective sensors, to provide a larger training data set, and conducting assessments at various locations exhibiting distinct atmospheric conditions.  
The quality of air in the modern day needs to be monitored, and the use of low-cost sensors is becoming more and more widespread as many people are turning to the supply of such sensors because they are affordable. Measurements from low-cost sensors can be reliable and trustworthy by applying appropriate optimization models.  
Conceptualization, I.S. and I.C.; methodology, I.S., E.S., O.T. and I.C.; software, I.C.; validation, I.S., E.S., O.T. and I.C.; formal analysis, E.S. and I.C.; investigation, I.C.; resources, I.C. and E.S.; data curation, O.T. and I.C.; writing--original draft preparation, I.C. and E.S.; writing--review and editing, O.T., E.S. and I.S.; visualization, I.C.; supervision, O.T. and I.S.; project administration, I.S.; All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
All of the data created in this study are presented in the context of this article.  
The authors declare no conflicts of interest.