## 2 Related Work  
The linear regression method has been applied to air pollution prediction within multiple research results. [PERSON] et al. present an application of a linear regression model regarding air pollutants (nitrogen dioxide, NO\({}_{2}\), particulate matter, PM10, and ozone, O\({}_{3}\)), as well as data related to meteorological and temporal factors [26]. These data were used to establish correlation between independent and dependent variables via the use of independent component analysis and principal component analysis. Input data to formulate the linear correlation function were obtained from monitoring stations in Indonesia for the period March-April 2002. It has been concluded that monitoring stations, used for data acquisition, provided data that resulted in different predictions due to meteorological factors (particularly wind) and pollutants interactions. Before creating a prediction model, it is necessary to select predictors, i.e., air and other components and factors, to be used in the formation of linear regression functions, which, in turn, are used in prediction. In order to provide better air quality assessments, [PERSON] et al. propose a new air quality evaluation model where environmental parameters (PM2.5, PM10, O\({}_{3}\), CO, NO\({}_{2}\), SO\({}_{2}\)) were evaluated with the application of fuzzy reasoning to compute and assign individual weights according to the pollutant importance on the air evaluation [6]. This model considers five air pollution score stages: excellent, good, regular, bad, and dangerous, based on data from the Mexico City Atmospheric Monitoring System. With the pollutant weights in place, a better evaluation model is proposed for the air quality assessments. [PERSON] and [PERSON] present the application of feature selection methods (based on Least Absolute Selection and Shrinkage Operator with the use of various machine learning techniques) in order to determine potentially significant predictors [27]. Conclusions were drawn based on exploratory data analysis in Delhi, India, and surrounding cities. It has been concluded that carbon monoxide, sulphur dioxide, nitrogen dioxide, and ozone are the most important factors affecting the air quality index. Diverse solutions enable data acquisition and collection in research related to air pollution prediction via the Internet of Things (IoT), cloud computing, big data, and Geographic Information Systems (GIS). [PERSON] et al. present smart city data collection through the utilization of IoT system sensors, where air quality prediction is conducted with the use of machine learning technologies [41]. The Google Earth Engine (GEE) cloud computing platform was used to obtain CO, NO\({}_{2}\), SO\({}_{2}\), and aerosol optical depth (AOD) data in Shandong Province, China, during the 2018-2020 period [10]. [PERSON] et al. describe the air quality forecasting system based on monitoring stations located at large distances [28]. They collect a large quantity of meteorological and air quality data, and Microsoft's cloud platform Azure is used to integrate the obtained data. Geographic information systems were used as data sources in air quality prediction within the research of [PERSON] et al. [42] and [PERSON] et al. [43]. The development of methods and tools for air pollution prediction has emerged as a significant domain in recent years, with research and professional results related to applied artificial intelligence, data mining, fuzzy systems, machine learning, and neural networks. [PERSON] and [PERSON] analyzed methods of data mining for the prediction of air pollution [29]. In their research, they selected data from atmospheric pollutants PM10, SO\({}_{2}\), NO\({}_{2}\), and O\({}_{3}\) and performed prediction by integrating different methods, such as a genetic algorithm, the linear method of stepwise fit, decision trees, and neural networks. [PERSON] et al. propose a system for forecasting the air pollution index by utilizing a supervised machine learning approach [9].