## 3 Results and Discussion
### Trend and Correlation Analysis for Input Parameter
Precipitation and snowfall were not observed meteorological phenomena due to the characteristics of the factor; therefore, the  
Figure 2: Daily average and 30\(-\)day moving average for (**a**) PM\({}_{10}\), (**b**) NO\({}_{2}\), (**c**) SO\({}_{2}\), (**d**) O\({}_{3}\), (**e**) CO, and (**f**) PM\({}_{2.5}\). days when they do not occur have a value of 0, and the value of median is 0 or close to 0 because precipitation and snowfall are not observed for over half of the year. Figure 4 shows the daily average and 30-day moving average of the meteorological data. Each meteorological factor exhibited a seasonal pattern. Temperature, humidity, and precipitation were high in summer and low in winter, and from the wind speed and atmospheric pressure are high in winter and low in summer. Snowfall is observed in winter due to its seasonal characteristics. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Factor**} & \multicolumn{2}{c}{**Mean**} & \multicolumn{2}{c}{**Median**} & \multicolumn{2}{c}{**Min**} & \multicolumn{2}{c}{**Max**} & \multicolumn{2}{c}{**Standard Deviation**} \\  & **NW** & **AD** & **NW** & **AD** & **NW** & **AD** & **NW** & **AD** & **NW** & **AD** \\ \hline T (\({}^{\circ}\)C) & 13.96 & 14.21 & 14.77 & 15.28 & \(-\)10.47 & \(-\)14.74 & 30.96 & 33.74 & 9.51 & 9.61 \\ RH (\%) & 66.99 & 66.99 & 67.98 & 68.16 & 31.46 & 15.94 & 93.91 & 100.00 & 13.00 & 15.86 \\ WS (m/s) & 2.11 & 2.18 & 1.95 & 1.91 & 1.05 & 0.35 & 5.42 & 12.67 & 0.66 & 1.11 \\ AP (hPa) & 1009.63 & 1009.52 & 1009.87 & 1009.45 & 985.88 & 978.47 & 1029.04 & 1033.59 & 7.74 & 8.22 \\ Precipitation (mm) & 0.61 & 0.39 & 0.16 & 0.00 & 0.00 & 0.00 & 10.86 & 32.50 & 1.01 & 1.18 \\ Snowfall (cm) & 0.22 & 0.06 & 0.00 & 0.00 & 0.00 & 0.00 & 11.00 & 20.35 & 0.88 & 0.59 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Statistical description of daily meteorological factor, 2015–2019. Figure 3: Daily standard deviation and 30\(-\)day moving average for (**a**) PM\({}_{10}\), (**b**) NO\({}_{2}\), (**c**) SO\({}_{2}\), (**d**) O\({}_{3}\), (**e**) CO, and (**f**) PM\({}_{2.5}\). Figure 5 shows the daily standard deviation of the meteorological data and 30-day moving average. In the standard deviation, seasonal patterns similar to the daily average patterns were observed. As shown in Figure 5, the standard deviations of temperature, humidity, wind speed, and atmospheric pressure are small in summer and large in winter, and the standard deviation in precipitation is large in summer and small in winter. Snowfall is observed exclusively in winters, the standard deviation appears only during winter. Because the air pollutant factor is known to affect the occurrence of PM\({}_{2.5}\) and meteorological factors reflect seasonal changes, they are appropriate as independent variables in the prediction of PM\({}_{2.5}\). To examine their relationship in greater depth, the correlation between the air pollutants and meteorological factors and the next-day PM\({}_{2.5}\) concentration was analyzed. Independent and dependent variables used to develop the prediction model are summarized in Table 1. Air pollutant and meteorological factors were independent variables, and next-day PM\({}_{2.5}\) concentration was the dependent variable to be predicted. As correlations using NW data and AD data are very similar, only the correlation with NW data is presented. Results of correlation analysis for next-day PM\({}_{2.5}\) and air pollutant factors are summarized in Table 6. The daily average concentrations of PM10 (0.57), NO\({}_{2}\) (0.62), SO\({}_{2}\) (0.52), CO (0.63), and PM\({}_{2.5}\) (0.71), which are air pollutants excluding O\({}_{3}\) (\(-\)0.02), showed a high positive correlation with next-day PM\({}_{2.5}\).