## 3 Materials and Methods
Results of executing the created R program (that includes utilization of the predict() R function) within the R GUI are shown in Figure 6. This is an example of successfully predicting future values of a CO pollutant based on the previously generated linear model of correlated CO and NO. Figure 6 presents three columns of data generated by the predict function for each item of the input data vector; there are computed values of prediction fit, but also lwr (lower) and upr (upper) values for each fit value. This way, it is obvious that the prediction function does not provide only one value, but an interval of values that could be expected in the future, using the underlying linear function as a basis. For this particular case of correlation between CO and NO values, Figure 7 presents a diagram that enables comparison between the computed prediction data and the real measurement data for CO as being dependent on the NO values data set in the previously presented prediction function. Figure 7 contains graphs representing predicted data for CO for each particular measurement of NO as a lower (min), fit (mid), and upper (max) predicted value, as well as a graph presenting CO measured values (at the same time and at the same monitoring station as the NO value that was used for prediction). Since the r correlation coefficient between NO and CO is 0.8515, it is obvious from Figure 7 that the measurements and prediction graphs for CO are not very closely aligned. The correlation heat map, presented at Figure 8, shows values of r correlation coefficients computed upon pairs of data vectors for all obtained parameters, including air pollutants and meteorological parameters. Figure 8: Correlation heat map with all obtained air pollution parameters from the sample. Figure 7: Diagram of comparatively presented real measurement data of CO and predicted values (range) of CO, based on linear correlation with NO values. According to the correlation heat map, the high correlation (r values from 0.9 to 0.7) was computed with pairs NO-NO\({}_{\rm x}\) (r = 0.9809), CO-NO\({}_{\rm x}\) (r = 0.8667), NO-CO (r = 0.8515), PM2.5-PM10 (r = 0.7895), NO\({}_{2}\)-NO\({}_{\rm x}\) (r = 0.7558), while for moderate correlation (r values from 0.7 to 0.5), there are also six air pollutant pairs. It could be concluded that the computed correlation between meteorological parameters and air pollutants is very low, with an r value less than 0.2. Therefore, in the rest of this paper, these pairs of parameters will not be presented with linear regression diagrams, predictions, and detailed statistics. The linear equations (as results of the linear regression method) are presented for selected (from the high, moderate, and low correlation categories) pairs of air pollutants as bivariate graphical plots (Figure 9a-f) that describe their mutual dependence, i.e., inter-variable correlations. Dots represent data from vectors, while lines, plotted between dots are the graphical representation of a derived linear equation from the data in vectors. A detailed statistical analysis of the correlation between the model and the data, apart from the graphical representation (quantity of dots placed near the line), can also be conducted on the basis of the statistical data presented in Tables 1-6. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{**Y = 3.413 + 0.698x**} & & & **r = 0.7895** \\ \hline PM2.5-PM10 & Std. error & \(t\)-value & \(p\)-value & Residual Std. error & F-statistic \\ \cline{2-6} (x-Y) & 0.0405 & 17.214 & \textless{}2 \(\times\) 10\({}^{-16}\) *** & 7.945 & 296.3 \\ \hline \multicolumn{6}{c}{Signif. codes: 0 ***.} \\ \hline \multicolumn{6}{c}{Signif. codes: 0 ***.} \\ \end{tabular}
\end{table} 
Table 1: Summary statistics from linear regression model for PM2.5 and PM10.