## 3 Materials and Methods  
Scientific data are often stored in formats not suitable for analysis and processing. Making applications that work with the diversity of data sources and growing databases has become an emerging topic since rapid data availability and processing could be a limiting factor for end-users [8]. The methodology and procedures used in this research include air pollution data collection, pre-processing with the use of the linear regression method, and processing in the prediction. Results of air pollutant values in this research are presented with the use of measurement units as mass units in the volume unit, taking them as related to time. These measurement units could be used over a long period of time for measuring concentrations of gases at shorter intervals [12]. Figure 1 presents a flowchart that visualizes the proposed method of using the R program within the air pollution prediction system. Data collection used in this research consists of air pollution data obtained from the official web site of the Environmental Protection Agency, affiliated with the Ministry of Environmental Protection, Belgrade, Republic of Serbia. Raw measurement data are presented at this website, since 2008, each hour of every day, obtained from automatic data acquisition stations that are located at different places, especially in cities (at crowded streets, industrial zones), but also at protected natural regions in the Republic of Serbia. Measurement data are presented comparatively for multiple measurement stations for each hour [46] or with a detailed data view for every measurement station, each day, and every hour [47]. The sample data for this research was downloaded from [47]. It consists of data related to the January-March period in 2021 and 2022 and particularly selected for Serbia's capital city, Belgrade. The observed period was from January to March in both years, since it was the winter period, when pollution is expected to be higher. Selecting a seasonal sample (winter) is aligned with the results of [PERSON] et al., where better accuracy of a mathematical model was created with linear regressions from seasonal data compared to annual data samples [32]. The City of Belgrade was selected as a very crowded urban area with large industrial and residential parts and heavy traffic. The sample consists of 378 measured air pollutant concentrations in 2021 and 567 measurements in 2022 for the following components: sulphur dioxide (SO\({}_{2}\)), particulate matter (PM10, PM2.5), carbon monoxide (CO), and nitrite oxides (NO, NO\({}_{2}\), and NO\({}_{\chi}\)). All sample air pollutant values were obtained from the data source as raw data represented with the use of the measurement unit \(\mathrm{\mu g/m^{3}}\), while CO values were represented with the mg/m\({}^{3}\) measurement unit. In addition to these measurements, the sample data also contain the following meteorological conditions: air temperature (T, Celsius scale), relative humidity (rh, percentage), and atmospheric pressure (p, millibar measures). The obtained sample data from the web site as a source was transformed into a table- delimited text file, suitable for loading into the R GUI (Figure 2), with a data structure consisting of city/time/SO\({}_{2}\)/NO\({}_{2}\)/NO\({}_{\chi}\)/CO/NO/PM2.5/PM10/p/T/rh. The T symbol is regularly used for temperature, while in this research, the sample presents temperature with the symbol t. Data vectors, created from the loaded text file, are used to obtain a linear regression function that establishes the correlation of measured values of two air pollutants. air pollution data. Finally, results of data processing (i.e., air pollutant value prediction) are organized according to categories of air pollutant concentrations and air quality: excellent, good, acceptable, polluted, very polluted, as defined by the Environmental Protection Agency [48].