## 1 Introduction  
Emissions of dangerous gases into the atmosphere due to accidents, human activities, natural disasters, or other reasons are great threats to the human population, nature, and infrastructure. Air pollution is one of the significant environmental problems that can cause adverse health effects, such as asthma, allergies, infections [1], cancer [2], and the risk of low birth weight [3]. These health-related issues are correlated with air pollution, particularly in traffic [4; 5]. Therefore, air pollutants data acquisition, measurements, monitoring, evaluation model formulation [6], assessment [7], benchmarking [8], and forecasting [7; 9] became increasingly important, particularly in the circumstances of pandemics [10]. Many efforts have been made in the development of appropriate air pollution-related methods and tools, as well as their integration [11].  
Data science software tools and modern programming languages enable working with large amounts of data related to environmental problems by utilizing specific functions and commands for data analysis, advanced reasoning, and visualization [12], with languages such as Python [13] and R [14]. R was developed as a computational environment to enable statistical data analysis by [PERSON] and [PERSON] from the University of Auckland, New Zealand [15]. R is a free and open-source programming language and software environment. The development and maintenance are assigned to the R Core Team, i.e., the R Foundation for Statistical Computing [16]. One of the most important features of R is its flexibility and scalability, i.e., the possibility to add a set of new functionalities to the base system. This is supported by tools for the development of additional packages [17], so, in this way, R could be used in a diverse set of applications, such as ecology [18].  
The air pollution data analysis and the use of R in ecology were in focus in multiple previous research results [19; 20; 21; 22; 23; 24; 25]. However, the literature review of related work shows the research gap, which provides the basis for this research. Other related work has focused on particular air pollutants and their predictions [9; 24; 26; 27; 28; 29; 30; 31; 32; 33; 34], while this paper provides predictions for a variety of air pollutants. Other papers have used linear regression in air pollution prediction but not with R language [28; 32; 33; 34; 35; 36; 37; 38; 39]. In previously published papers, R was used for air pollution data analysis, but with other methods not with linear regression [23; 24; 25; 40].  
The aim of this research is related to using R for air pollution data correlation and prediction. R language statements are used for creating the program that performs data pre-processing with linear regression and establishes the mathematical model for the relationship between two variables. In this research, the dependent and independent variables are selected among air pollutant values for PM2.5, PM10, NO, NO\({}_{2}\), NO\({}_{\rm X}\), CO, and SO\({}_{2}\), as well as meteorological parameters-pressure, temperature, and relative humidity. The obtained mathematical models are used for air pollution data prediction. Results of this study include: (1) a detailed presentation of the developed R program for linear regression and prediction of air pollution data; (2) results of correlation of particular air pollutants, particularly presented with linear regression diagrams; (3) results of linear fitting, i.e., statistical evaluation of linear functions preciseness; and (4) prediction results based on previously obtained and evaluated mathematical functions that correlate air pollutant values.