Investigation of LASSO Regression Method as a Correction Measurements' Factor for Low-Cost Air Quality Sensors  
[PERSON]  
Electronic Devices and Materials Laboratory, Department of Electrical and Electronics Engineering, Faculty of Engineering, University of West Attiica, Thivon Av. 250, GR-12241 Athens, Greece; [EMAIL_ADDRESS] (E.S. ); [EMAIL_ADDRESS] (O.T. ); [EMAIL_ADDRESS]  
[PERSON]  
Electronic Devices and Materials Laboratory, Department of Electrical and Electronics Engineering, Faculty of Engineering, University of West Attiica, Thivon Av. 250, GR-12241 Athens, Greece; [EMAIL_ADDRESS] (E.S. ); [EMAIL_ADDRESS] (O.T. ); [EMAIL_ADDRESS]  
[PERSON]  
Electronic Devices and Materials Laboratory, Department of Electrical and Electronics Engineering, Faculty of Engineering, University of West Attiica, Thivon Av. 250, GR-12241 Athens, Greece; [EMAIL_ADDRESS] (E.S. ); [EMAIL_ADDRESS] (O.T. ); [EMAIL_ADDRESS]  
[PERSON]  
Electronic Devices and Materials Laboratory, Department of Electrical and Electronics Engineering, Faculty of Engineering, University of West Attiica, Thivon Av. 250, GR-12241 Athens, Greece; [EMAIL_ADDRESS] (E.S. ); [EMAIL_ADDRESS] (O.T. In both cases, the objective in calibration [18] is to find a function \(F\) such that the measurement values from the low-cost sensors \(x_{t}\) approximate the values of the reference instruments \(y_{t}\) (Equation (1)). \[y_{t}=F\left(x_{t}\right) \tag{1}\]  
In addition to the calibration process of low-cost sensors, a large number of research works have been carried out on the optimization of measurements from them. The application of linear regression and multiple linear regression to measurements of low-cost air quality sensors have shown correction factors with satisfactory results on the optimized measurements. Regression is one of the basic methods of analysis, as it is based on data used due to their simplicity and high degree of scalability [19]. The research works [20; 21] report the use of linear regression, multivariate linear regression, and an artificial neural network to calibrate a cluster of low-cost O\({}_{3}\), NO\({}_{2}\), NO CO, and CO\({}_{2}\) sensors. Experimental results show that the artificial neural network can mitigate the effect of cross-sensitivity. In another work [22], calibration on a large number (more than 100) of metal-oxide ozone sensors of air pollution was performed using multiple linear regression. The research group [23] proposes a method for the calibration of portable gas sensors for accurate measurement of O\({}_{3}\) and NO\({}_{2}\) by utilizing linear regression, least squares, and major axis linear regression. In the research work [24], the method of correcting measurements according to the quantitative variation in low-cost sensor measurements using linear regression is proposed. Non-linear regression methods have been applied to optimize measurements in low-cost gas quality sensors. The work [25] shows the application of linear regression, multiple linear regression, and nonlinear regression to microparticles sensors, where the results of both multiple linear regression and nonlinear regression were equally good. According to the literature, many research papers [26; 27; 21; 22; 28] focus on sensor signal processing using nonlinear machine-learning algorithms (such as random forest (RF), artificial neural networks (ANN), support vector machine (SVM) methods, and the Kalman filtering (KF) approach) for the calibration of low-cost air quality sensors. In addition, advanced machine-learning (ML) methods, such as neural networks, have been applied to improve measurements from low-cost sensors, which yield better optimization as they are able to solve complex problems due to their robust performance [29; 30; 31]. The calibration of a low-cost sensor, when achieved by colocation next to reference instruments, requires a long measurement period to extract an effective calibration equation.