## 4 Results and Discussions  
First, compared the PM\({}_{2.5}\) estimation of all MLR models using different satellite AOD data (Table 4). For the M1 and M2 models, the performance of PM\({}_{2.5}\) estimation seems largely dependent on the used AOD; the cases of MODIS and MISR AOD show higher correlation and lower RMSE, but those of VIIRS and OMI AOD show lower correlation and higher RMSE. However, the M3 model results reveal the consistency of PM\({}_{2.5}\) estimation for all AOD cases. All correlation coefficients are higher 0.7 and their difference is smaller than 0.2; these values are significantly better than the cases of the M1 and M2 models, which show correlations < 0.5 and correlation differences > 0.3. In terms of slope, M1 and M2 show relatively small slopes: all slopes of M1 and M2 < 0.3. However, M3 results show a significantly better slope, which is from 0.5 to 0.7. Comparison between M3-1 and M3-2 indicates that M3-1 can estimate PM\({}_{2.5}\) slightly better, implying that the ground-based measurement of PBLH can better represent the local conditions of aerosol distribution. Overall, MODIS-DT AOD results in the best quality of PM\({}_{2.5}\) estimation: the highest correlation coefficient, smallest RMSE, and largest slope. Other MODIS AODs (MODIS-DB and MODIS-3 km) show a similar performance. MISR AOD shows a high correlation and large slope, but RMSE is high, probably due to the small data number (Table 4). VIIRS AOD does not result in high accuracy of PM\({}_{2.5}\) estimation in the M1 and M2 models; in particular, the slope is relatively small (<0.1), indicating a weak sensitivity of the estimated PM\({}_{2.5}\) to the observed value. However, VIIRS AOD results in highly enhanced accuracy of PM\({}_{2.5}\) estimation in the M3 model. The reason for this drastic enhancement is not clear,  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Statistics** & **MLR Model** & **MODIS-DT** & **MODIS-DB** & **MODIS-3 km** & **VIIRS** & **MISR** & **OMI** \\ \hline \multirow{8}{*}{R} & M1 & 0.4 & 0.48 & 0.36 & 0.29 & 0.36 & 0.17 \\  & M2-1 & 0.43 & 0.54 & 0.43 & 0.21 & 0.45 & 0.24 \\  & M2-2 & 0.49 & 0.46 & 0.46 & 0.  
but perhaps can be explained by the larger number of data than that of other AODs. In turn, this is attributable to a larger swath width because statistical modeling is generally more advantageous with a larger dataset. OMI AOD results in relatively lower quality of PM\({}_{2.5}\) estimation, but the M3 result is acceptable, showing a moderate correlation coefficient (R = -0.7). The low spatial resolution of OMI L1B data (13 \(\times\) 24 km\({}^{2}\)), and generally unfavorable conditions of cloud masking in aerosol retrieval, may result in a lower correlation coefficient. Here, we confirm that the choice of AOD to be applied can influence the performance of PM\({}_{2.5}\) estimation, but that any AOD can contribute to the moderate quality of PM\({}_{2.5}\) estimation when the MLR model is composed of PBLH with other meteorological and chemical information. Based on the result of the M3-1 model, which recorded the best performance, the seasonal pattern of estimated PM\({}_{2.5}\) was also analyzed (Figure 2). Compared to the observed PM\({}_{2.5}\), the estimated PM\({}_{2.5}\) shows the highest correlation (R = -0.8 to 0.9) and slope (-0.7 to 0.9) in autumn (September-October-November, SON), implying that the columnar AOD has a relatively high sensitivity to the variation in surface fine-mode particles during this season. Given that there are a number of springtime Asian dust events and summertime hygroscopic growth of local aerosols in the Korean peninsula [39; 40], AOD in spring and summer can be partially affected by coarse-mode particles, resulting in relatively lower correlations and slope values. The RMSE pattern looks similar, i.e., small RMSE in autumn (i.e., higher quality) but large RMSE in spring (i.e., lower quality). RMSE in summer is relatively small because PM\({}_{2.5}\) is the lowest in summer.