## 3 Results and Discussion  
### Trend and Correlation Analysis for Input Parameter  
Table 4 displays the basic statistics of air pollutant factors for nationwide and administrative districts. Based on these, Table 4 and the South Korea comprehensive air quality index (Table S1), PM\({}_{10}\) and PM\({}_{2.5}\) have normal levels on an average, whereas NO\({}_{2}\), SO\({}_{2}\), O\({}_{3}\), and CO show good concentrations. However, the maximum values in Table 4 show harmfully high levels of PM\({}_{10}\) and PM\({}_{2.5}\), and bad levels of NO\({}_{2}\) and O\({}_{3}\) depending on the region. Table S2 provides in-depth details of the PM\({}_{2.5}\) factor, which is the response variable in this study. Over 80% of PM\({}_{2.5}\) daily averages were good or normal, whereas very bad concentrations were scarce. Figure 2 shows the daily average of air pollutant data and the 30-day moving average of the daily average. Each air pollutant factor had a seasonal pattern; PM\({}_{10}\), NO\({}_{2}\), SO\({}_{2}\), \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Average**} & \multicolumn{2}{c}{**Median**} & \multicolumn{2}{c}{**Min**} & \multicolumn{2}{c}{**Max**} & \multicolumn{2}{c}{**Standard Deviation**} \\
**Factor** & **NW** & **AD** & **NW** & **AD** & **NW** & **AD** & **NW** & **AD** & **NW** & **AD** \\ \hline PM\({}_{10}\)(ug/m\({}^{3}\)) & 44.49 & 43.34 & 41.24 & 39.11 & 7.79 & 5.19 & 377.21 & 555.69 & 21.77 & 22.848 \\ NO\({}_{2}\) (ppm) & 0.022 & 0.020 & 0.021 & 0.018 & 0.006 & 0.003 & 0.055 & 0.082 & 0.008 & 0.010 \\ SO\({}_{2}\) (ppm) & 0.004 & 0.004 & 0.004 & 0.004 & 0.003 & 0.001 & 0.011 & 0.025 & 0.001 & 0.002 \\ O\({}_{3}\) (ppm) & 0.028 & 0.029 & 0.027 & 0.028 & 0.006 & 0.001 & 0.074 & 0.092 & 0.011 & 0.012 \\ CO (ppm) & 0.490 & 0.477 & 0.457 & 0.446 & 0.277 & 0.113 & 1.058 & 1.485 & 0.129 & 0.151 \\ PM\({}_{2.5}\) (ug/m\({}^{3}\)) & 24.634 & 24.633 & 22.573 & 21.917 & 3.393 & 1.045 & 106.897 & 140.793 & 12.243 & 13.898 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Statistical description of daily air pollutant factor, 2015–2019. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **1 \(\sigma\)** & **0.5 \(\sigma\)** & **0.25 \(\sigma\)** \\ \hline Value & 12 & 6 & 3 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Absolute error threshold for n \(\sigma\). and CO showed a decreasing trend every year, whereas O\({}_{3}\) showed an increasing trend. PM\({}_{10}\) was high in the winter and low in the summer. NO\({}_{2}\), SO\({}_{2}\), and CO also show patterns similar to those of PM\({}_{10}\). PM\({}_{2.5}\) also shows a similar pattern to PM\({}_{10}\), as shown in Figure 2. Conversely, O\({}_{3}\) was high in summer and low in winter. Figure 3 shows the daily standard deviation of the air pollutant data and its 30-day moving average. A seasonal pattern in the standard deviation was also observed. As shown in Figure 3, the daily standard deviation of PM\({}_{2.5}\) varies seasonally, and the variance is large in winter and small in summer. As for NO\({}_{2}\), SO\({}_{2}\), and CO, the standard deviation in summer decreases each year. As shown in Figure 3 and, CO concentration and its standard deviation are high in winter, low in summer, and decreases every year. As shown in Figure 3, the standard deviation of O\({}_{3}\) was large in summer and small in winter. As shown in Figures 2 and 3, PM\({}_{10}\), O\({}_{3}\), CO, and PM\({}_{2.5}\), all had large standard deviations when the daily average was high and small standard deviations when the daily average was low. Table 5 presents the basic statistics of the meteorological factors, where precipitation indicates the amount of rain per hour, and snowfall indicates the height of snow accumulation from the hourly indicator. Therefore, the daily average snowfall represents the average daily snow height from the ground. In the case of the daily average snowfall, there was a significant difference between administrative districts.