## 2 Related Work
[PERSON] and [PERSON] presented results in creating multiple statistical models for prediction of PM10, PM2.5, and PM1 based on local meteorological parameters (air temperature, wind speed, and relative humidity), PM10 and PM2.5 concentrations, and dust periods [33]. This statistical modeling has been created based on the data from Beijing, China, and applied to Gangneung, Korea. The correlation among PM10, PM2.5, and PM1 concentrations is represented as multiple correlation coefficients, and the prediction of PM concentration has shown a significant level of multi-regression significance when the observed and calculated PM concentrations were compared. The prediction of the next day's hourly ozone concentration was studied in [37]. In this research, feedforward artificial neural networks are proposed to use principal components as inputs. The developed model was compared with multiple linear regression, feedforward artificial neural networks based on the original data, and also with principal component regression. It has been shown that the proposed use of principal components reduced complexity in the application of compared methods and eliminated data collinearity. Land-use regression (LUR) models are used to estimate air pollution in epidemiologic research, and they use data obtained from a small set of locations, so geographical location could not impact the prediction results. [PERSON] et al. presented one of such studies that use LUR, where the health effects of air pollution were examined [38]. Land-use regression has been used at the regional level (national level in the USA) regarding NO\({}_{2}\) measurements and predictions performed with satellite data [39]. The basic R system Is constantly enhanced by several hundreds of contributed packages that cover a wide array of modern statistical methods and application areas [14]. With millions of lines of R code available in repositories stored on the Internet, researchers and programmers have an opportunity to use a combination of static and dynamic program features for various analyses [45]. In recent years, the R language has been frequently used in scientific research, especially in ecology and environmental protection. R was used for weather monitoring and rain gauge observation [19]. The result was a free and open-source R program with the purpose of computing merged data from radar and rain gauges. Another example of using R is described in the work of [PERSON] et al., who introduce Picante, a package created to extend R with tools for analyzing phylogenetic and trait diversity of ecological communities, calculating phylogenetic diversity metrics, performing trait comparative analyses, etc. [20]. [PERSON] et al. present an additional R package, rFIA, created for the purpose of forest data analysis with the use of the FIA database, which was created to support monitoring of changes in forests across the USA [21]. R has been used for creating an image analysis framework designed to detect land cover types and vegetation corresponding to the spectral reflectance of the objects represented on the Earth's surface [22]. The R model is used with images obtained from sensor scenes (Landsat-8 Operational Lang Imager and Thermal Infrared Sensor). The image data were processed by using different auxiliary packages of R. Results of using the R-based image analysis framework were created based on time series of the images taken at various periods to monitor the landscape dynamics in the Congo River basin. [PERSON] et al. present results of using R as a statistical software to analyze a large amount of air pollution data in Korea [23]. [PERSON] presents results that are related to utilizing R and R Studio for the prediction of nitrogen dioxide pollution, with particular emphasis on the comparison of the autoregressive integrated moving average and the exponential smoothing model [24]. [PERSON] and [PERSON] describe openair as an R package developedfor the analysis of air pollution measurement data, but it could also be used in broader atmospheric sciences [25].