## 3 Materials and Methods
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{**Y = 143.66x – 94.94**} & & & **r = 0.8515** \\ \hline CO-NO & Std. error & \(t\)-value & \(p\)-value & Residual Std. error & F-statistic \\ \cline{2-6} (x-Y) & 6.751 & 21.28 & \textless{}2 \(\times\) 10\({}^{-16}\) *** & 19.3 & 452.9 \\ \hline \multicolumn{6}{c}{Signif. codes: 0 ***.} \\ \end{tabular}
\end{table} 
Table 2: Summary statistics from linear regression model for CO and NO. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{**Y = 143.66x – 94.94**} & & & & **r = 0.8515** \\ \hline CO-NO & Std. error & \(t\)-value & \(p\)-value & Residual Std. error & F-statistic \\ \cline{2-6} (x-Y) & 6.751 & 21.28 & \textless{}2 \(\times\) 10\({}^{-16}\) *** & 19.3 & 452.9 \\ \hline \multicolumn{6}{c}{Signif. codes: 0 ***.} \\ \end{tabular}
\end{table} 
Table 3: Summary statistics from linear regression model for SO\({}_{2}\) and NO\({}_{2}\). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{**Y = 8.428 + x39.489**} & & & **r = 0.6305** \\ \hline CO-NO\({}_{2}\) & Std. error & \(t\)-value & \(p\)-value & Residual Std. error & F-statistic \\ \cline{2-6} (x-Y) & 3.401 & 11.609 & \textless{}2 \(\times\) 10\({}^{-16}\) *** & 9.722 & 134.8 \\ \hline \multicolumn{6}{c}{Signif. codes: 0 ***.} \\ \end{tabular}
\end{table} 
Table 5: Summary statistics from linear regression model for NO and NO\({}_{\rm X}\). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{**Y = 8.428 + x39.489**} & & & **r = 0.6305** \\ \hline CO-NO\({}_{\rm X}\) & Std. error & \(t\)-value & \(p\)-value & Residual Std. error & F-statistic \\ \cline{2-6} (x-Y) & 3.401 & 11.609 & \textless{}2 \(\times\) 10\({}^{-16}\) *** & 9.722 & 134.8 \\ \hline \multicolumn{6}{c}{Signif. codes: 0 ***.} \\ \end{tabular}
\end{table} 
Table 5: Summary statistics from linear regression model for NO and NO\({}_{\rm X}\). Figure 9: Linear regression diagrams created within R GUI with R program (**a**) PM10-PM2.5; (**b**) CO-NO; (**c**) SO2-NO2; (**d**) NO-NOX; (**e**) CO-NO2; (**f**) NO2-NOX. Standard statistical parameters such as [PERSON]'s criterion (F), correlation coefficient (r), estimate and residual standard error, \(p\)-value, and \(t\)-value were used for model validation. According to statistical parameters, it can be seen from Tables 1-6 that we have a moderate, high, and very high positive correlation with the model. The best correlation and fit to the data is with NO-NO\({}_{\rm X}\) pollutants (r = 0.9809), which had a high F-value and low \(p\)-values and standard errors. The correlation between CO and NO pollutants was strong (r = 0.8515), which can be seen by statistical parameters and a graph. The very high correlation was in the case of PM2.5-PM10 (r = 0.7895) and NO\({}_{\rm 2}\)-NO\({}_{\rm X}\) (r = 0.7558), as were the high F-value and low \(p\)-value and standard errors. CO-NO\({}_{\rm 2}\) air pollution showed a moderate correlation with the model (r = 0.6305). The weakest interdependence was shown by SO\({}_{\rm 2}\)-NO\({}_{\rm 2}\) pollutants (r = 0.4468), who had the lowest F-value and the greatest dispersion of data. A good correlation between NO\({}_{\rm 2}\), NO, and NO\({}_{\rm X}\) (r-0.68 to 0.98) can be explained by the chemical similarity of the compounds and the chemical pathway of their formation [49]. \[{\rm N}_{\rm 2}+{\rm O}_{\rm 2}\to 2{\rm NO}, \tag{1}\]  
\[{\rm NO}+{\rm O}\to{\rm NO}_{\rm 2}, \tag{2}\]  
\[{\rm NO}+{\rm O}_{\rm 3}\to{\rm NO}_{\rm 2}+{\rm O}_{\rm 2}, \tag{3}\]  
According to the correlation heat map (Figure 8), there is a weak correlation (r < 0.5) between chemically different oxides of carbon, sulphur, and nitrogen. The specific case is CO, which establishes a high and medium correlation with all air pollutants, except with SO\({}_{\rm 2}\) (r = 0.3349). Table 7 shows the summarized and categorized results of predicting air pollutant values for SO\({}_{\rm 2}\), PM10, PM2.5, CO, and NO\({}_{\rm 2}\).