## 1 Introduction  
Air quality is a vital factor for human health, particularly in densely populated areas. Low-cost sensors have emerged on the market in recent years as a consequence of the development of technology, presumably also because of demand or the growth of a market for small-scale measurements. Citizens are increasingly using these sensors to monitor the air quality in their area. Research centers and governmental agencies are engaged in monitoring air quality in all countries, using expensive scientific instruments to ensure the reliability of the measurements, resulting in only a few such systems covering large areas, like cities [1]. The use of low-cost sensors can help develop a large spatial network of measurements for a region [2]. Low-cost sensors can be used as a complement to monitor the air quality of a region and not as a replacement for expensive scientific equipment [3]. The reliability of low-cost sensor measurements is an ongoing area of research and study in the scientific community. The following research papers present methodologies aiming at correcting and visualizing the measurements obtained from these sensors. Many manufacturers and research groups have devoted themselves to developing low-cost sensors and evaluating their performance in laboratory conditions [4; 5; 6; 7]. Low-cost electrochemical air quality sensors use a specific measurement methodology [8; 9; 10]. All low-cost sensors exhibit errors, resulting in differences in measurement data compared to reference data. There are two main sources of error in measurements from low-cost sensors [11], the internal errors, which are based on the operating principle of the sensor, including dynamic detection limits, nonlinear sensor response and displacement, and the external errors, which occur according to the working conditions, with the main one the low selectivity of the target gas, affected by cross-sensitivity with other gases. These errors can be reduced using laboratory and simplified calibration methods. Therefore, many investigations into low-cost sensors have demonstrated the development of sensor calibration in the field [12]. These include low-cost sensor calibration of carbon monoxide (CO) [13; 14], nitrogen dioxide (NO\({}_{2}\)) [13; 14; 15], sulfur dioxide (SO\({}_{2}\)) [13; 14], ozone (O\({}_{3}\)) [13; 15], and particulate matter (PM) [16; 17; 18]. In [19] the evaluation of the performance of low-cost particle sensors indicated the ability of the sensors to monitor particle concentrations with relatively high linearity and moderate repeatability. In another work [20], the effect of relative humidity (RH) and gas sample flow rate on sensor calibration and sensitivity was investigated. The performance of the low-cost sensors was evaluated in the field, in an ambient atmosphere. Many of the aforementioned studies focused on the influence of meteorological factors, such as temperature (T), RH, wind, sunlight, etc., and proposed calibration schemes based on long-term evaluation in urban or suburban environments [21; 22; 23; 24]. In addition, other studies have reported on the effect of cross-sensitivity in low-cost sensors [25; 26; 27]. Many methods have been used by research groups to calibrate air quality sensors, such as linear regression (LR), multiple linear regression (MLR), random forest (RF), artificial neural network (ANN), support vector machine (SVM), and support vector regression (SVR) [28]. The LR and MLR methods are commonly for calibration of low-cost sensor data, and symmetric variables are used to improve the calibrations. However, LR shows a lower R\({}^{2}\) correlation score and should be avoided for gas sensors [29; 30]. RF, ANN, SVM, and SVR are supervised learning technique methods and show significant improvement in the R\({}^{2}\) correlation coefficient in most sensors. However, their application requires a large amount of data for training each model [31; 32].